Comprehensive Areal Model of Earthquake-induced Landslides: 

Technical Specification and User Guide

By

Scott B. Miles and David K. Keefer

Open-File Report 2007-1072

2007

This report has not been reviewed for conformity with U.S. Geological Survey editorial

standards or with the North American Stratigraphic code. Any use of trade, product, or firm

names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

U.S. DEPARTMENT OF THE INTERIOR

U.S. GEOLOGICAL SURVEY
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Abstract

This report describes the complete design of a comprehensive areal model of earthquakeinduced landslides (CAMEL). This report presents the design process, technical specification of

CAMEL, and a guide to using the accompanying source code and template ESRI ArcGISTM map 

document file for applying CAMEL. CAMEL is a regional-scale model of earthquake-induced

landslide hazard developed using fuzzy logic systems. CAMEL currently estimates areal

landslide concentration (number of landslides per square kilometer) of six aggregated types of

earthquake-induced landslides – three types each for rock and soil.
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Table of Contents

1 INTRODUCTION ....................................................................................................................................................... 4

2 FUZZY LOGIC SYSTEMS....................................................................................................................................... 4

2.1 ELEMENTS OF FUZZY LOGIC SYSTEMS ................................................................................................................. 5

2.2 DESIGN OF FUZZY LOGIC SYSTEMS..................................................................................................................... 12

3 CAMEL KNOWLEDGE ELICITATION ............................................................................................................ 15

4 CAMEL DESIGN AND SPECIFICATION.......................................................................................................... 17

4.1 POSSIBILITY MODULE .......................................................................................................................................... 18

4.2 HAZARD MODULE ................................................................................................................................................ 29

5 CAMEL IMPLEMENTATION AND USE........................................................................................................... 49

5.1 IMPLEMENTATION OF CAMEL............................................................................................................................ 49

5.2 INTEGRATION OF CAMEL WITH ARCGIS .......................................................................................................... 50

6 REFERENCES .......................................................................................................................................................... 55

APPENDIX A: COMPLETE SPECIFICATION OF HAZARD MODULE RULE-BLOCKS FOR CAMEL

.............................................................................................................................................................................................. 57

A.1 ROCK AVALANCHE ............................................................................................................................................. 57

A.2 ROCK FALL AND DISRUPTED ROCK SLIDES ....................................................................................................... 59

A.3 ROCK SLUMPS AND ROCK BLOCK SLIDES ......................................................................................................... 61

A.4 DISRUPTED SOIL SLIDES, SOIL FALLS, AND SOIL AVALANCHES...................................................................... 63

A.5 SOIL SLUMPS AND SOIL BLOCK SLIDES ............................................................................................................. 66

A.6 RAPID SOIL FLOWS.............................................................................................................................................. 67
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1 Introduction

This report describes the complete design of a comprehensive areal model of earthquakeinduced landslides (CAMEL). This report presents the design process, technical specification of

CAMEL. It also provides a guide to using the CAMEL source code and template ESRI

ArcGISTM map document file for applying CAMEL, both of which can be obtained by contacting

the authors. CAMEL is a regional-scale model of earthquake-induced landslide hazard developed 

using fuzzy logic systems. CAMEL currently estimates areal landslide concentration (number of

landslides per square kilometer) of six aggregated types of earthquake-induced landslides – three

types each for rock and soil.

The report is presented in four sections. The first section describes the salient aspects of

fuzzy logic systems to aid understanding of the CAMEL technical specification, presenting first

general concepts and then a discussion of the fuzzy systems design process. The reader is

referred to Berkan and Trubatch (1997) and Mendel (2001) for an in-depth discussion of fuzzy

logic theory and design guidance, which not needed for use of CAMEL. The second section 

discusses the knowledge on which CAMEL was designed and how this knowledge was elicited.

The third section specifies the structure, elements, and functionality of the fuzzy logic system

that forms CAMEL, with a complete specification given as an appendix. The final section of this

report describes the current implementation of CAMEL in ESRI’s ArcGISTM software. The

section describes how to set up and use CAMEL with ESRI ArcMapTM.

2 Fuzzy Logic Systems

Fuzzy logic systems are based on the concept of fuzzy sets. Fuzzy sets were introduced

by Zadeh (1965) and are an infinite-valued extension of Boolean or “crisp” sets. Fuzzy sets

represent the degree to which something is a member of a particular category, as opposed to the

Boolean concept that category membership is represented as either completely true or false.

Fuzzy logic systems are a series of interdependent blocks of IF-THEN rules, which relate input

variables to a particular output variable. Based largely on Berkan and Trubatch (1997), the

remainder of the section defines and illustrates the fundamental concepts of fuzzy logic systems
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and goes on to describe the general fuzzy logic systems design process.

2.1 Elements of Fuzzy Logic Systems

2.1.1 Fuzzy Sets

Fuzzy sets expand on the concept of crisp Boolean sets by representing the level of

membership to a particular category – zero (0) indicating that membership is unambiguously false

and one (1) indicating membership is unambiguously true. An example is useful to understand 

the differences between crisp and fuzzy sets. Take for example three sites: Site A is 23 

kilometers from a fault zone, Site B is 27 kilometers away, and Site C is 75 kilometers away 

(Figure 1). Are these sites close to the seismic source? A crisp set requires definition of some

function––typically a numerical range––to determine whether or not each site is a member of the

category “close.” Using this function, each site’s distance value can be tested to determine

whether membership is true (1) or false (0). If a range of 0 to 25 kilometers is chosen to crisply

define “close”, it is true (1) that Site A is a member of “close”, while it is false (0) for Sites B and 

C. Pairing the site distances with the truth-value (0 or 1) can represent this set: 23km/1.0,

27km/0.0, 75km/0.0). Sites A and B are only 4 kilometers apart, but A is classified as being close

to the source and B is not.

With fuzzy sets, category membership is represented as a value between and including 0 

and 1. This value is referred to as a truth value. The membership function in fuzzy logic then 

specifies not only where membership is true (1) and false (0), but how the truth value changes

between 0 and 1 (y-axis) with respect to the function’s domain (x-axis). Membership functions

can have many different mathematical definitions, with the simplest and most commons ones

being piece-wise linear functions. Membership functions have two fundamental requirements:

(1) the function monotonically increases or decreases between 0 and 1; (2) the function has only 

one point (or region of points) corresponding to 1. For our example asking what sites are close to 

the seismic source, Figure 1 illustrates an arbitrary membership function defining the possible

fuzzy sets for the category “close.” The fuzzy set for this example then would be each distance

value with the associated truth-value: 23km/0.6, 27km/0.4, 75km/0.0.
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Figure 1. Example of crisp and fuzzy sets describing “close.”

2.1.2 Fuzzy Variables

A variable in fuzzy logic systems is comprised of a series of membership functions

defined with respect to a numeric domain. These membership functions are associated with the

possible categories or “fuzzy values” of a particular variable. Each fuzzy value is typically 

assigned and referred to by a category label. Figure 2 shows an example of a variable Slope Angle,

which is composed of the values “shallow,” “moderate,” and “steep” over the domain 0 to 50 

degrees from the horizontal. For example, the statement “Slope Angle IS steep” indicates the

variable Slope Angle has a value of “steep.” Considering the membership function used to define

“steep” shown in Figure 2, it is true that a slope having an angle from the horizontal of 35

degrees is “steep” to a degree of 0.7. That is, the truth value associated with the statement “Slope

Angle IS steep” is 0.7 for a slope angle of 35.
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Figure 2. Example of the variable Slope having values of “shallow,” “moderate,” and

“steep.” The statement “Slope Angle IS steep” is associated with truth value of 0.7, if the

slope angle is 35 degrees.

2.1.3 Fuzzy Rules

A rule within fuzzy logic systems is a computable IF-THEN statement relating variables.

A simple IF-THEN rule can be constructed using one variable for the input and one variable for

the output. An example of a rule is “IF Slope Angle IS steep THEN Landslide Hazard IS high”

(Figure 3). The truth-value related to the input statement “Slope Angle IS steep” is basically 

assigned to the output statement “Landslide Hazard IS high.” In the example of Figure 3, a slope

value of 35 degrees results in a truth-value of 0.7 from the membership function for “steep,”

which is then passed on to the output variable value indicated in the IF-THEN rule; in this case

“Landslide Hazard IS high.” In other words, if ”Slope Angle IS steep” with a truth value of 0.7,

then the truth value for ”Landslide Hazard IS high” is also 0.7. It is possible to assign a weight

between 0 and 1 to any rule, which is multiplied to the output truth value. This weight is called 

the degree of support (DoS) weight. A DoS weight less than one reduces the influence of a

particular rule.
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Figure 3. Example of passing on the input truth value to the output of the rule “IF Slope

Angle IS steep THEN Landslide Hazard IS high.”

2.1.4 Combining Input Values for a Single IF-THEN Rule

More complex IF-THEN rules can be constructed by combining multiple input variables

using operators like AND and OR. These operators are conceptually the same as Boolean

operators of the same name. The three most common fuzzy operators are AND (which passes

on the lower truth-value associated with two statements), OR (which passes on the larger truthvalue associated with two statements), and PROD (which passes on the result of multiplying the

truth-values associated with two statements). Consider a two-input rule, such as “IF Slope

Angle IS steep AND Distance IS close THEN ....” Assuming the truth value associated with 

“Distance IS close” is 0.4 and the truth value for “Slope Angle IS steep” is 0.7, applying the

AND operator results in a resultant truth value of 0.4, which corresponds to the smaller of the

two input truth-values. Combing the statements using the OR operator would result in a truthvalue of 0.7 – the larger of the input truth-values. Application of the PROD operator results in a

combined truth-value of 0.28 – the product of the two input truth-values. Any number of fuzzy

values can be combined using these operators.

2.1.5 Combining Multiple Rules with the Same Output Values

A fuzzy logic rule-block is a collection of fuzzy IF-THEN rules, with each rule mapping
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from any number of input variables to the same output variable. A complete rule-block will have

a minimum of one rule for each output variable value (that are each associated with a membership

function). Most rule-blocks, in fact, have more than one rule for each output variable value. In 

such cases, it is necessary to aggregate the multiple truth values determined from the multiple

rules that map to the same output variable value. One aggregation operator that can be used is

MAX, which takes the maximum truth-value of all the truth-values computed for a particular

output variable value. Alternatively, the BSUM (for “bounded sum”) aggregation operator can be

used, which takes the bounded sum, that can’t exceed 1.0, of all the truth values computed for a

particular output fuzzy value. Table 1 lists an example rule-block containing four rules. There

are two rules (rules 3 and 4) in which the output is “Landslide Hazard IS high.” Consider if the

output truth-value for rule 3 is 0.7 and is 0.5 for rule 4. Using MAX to aggregate the truth values

for “Landslide Hazard IS high” would result in an aggregate truth value of 0.7 (the maximum of

0.5 and 0.7). With BSUM, the sum of the two truth-values for “Landslide Hazard IS high”

would exceed 1.0 and so would result in a aggregate truth-value of 1.0.

Table 1. A four-rule fuzzy rule-block relating Slope Angle and Distance to Landslide

Hazard.

1. IF Slope Angle IS shallow THEN Landslide Hazard IS low

2. IF Slope Angle IS moderate AND Distance IS close THEN Landslide Hazard IS medium

3. IF Slope Angle IS steep THEN Landslide Hazard IS high

4. IF Lithology IS landslide deposit THEN Landslide Hazard IS high

2.1.6 Combining Rules with Different Output Values

To determine the overall result of a rule-block, all the contained rules are computed based 

on specific input data. The combined truth-values associated with each output value can be

directly interpreted. For example, output truth-values for the rule-block of Table 1 might be

determined as: “Landslide Hazard IS low” equal to 0; “Landslide Hazard IS medium” equal to 0.3;

“Landslide Hazard IS high” equal to 1.0. The interpretation of this result would be that landslide

hazard is unambiguously not low, but is partially medium and partially high, with evidence from

input data indicating that the landslide hazard is significantly more high than medium.
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Alternatively, the truth-values for each of the output variable values can be defuzzified, meaning

that a scalar value is determined within the output variable domain. There are several

defuzzification methods. For modeling applications, the most common method is called the

Center of Maximum (CoM) method. The CoM method takes a weighted average of the output

variable domain values, using as weights the truth value associated with the center of each output

variable membership function. Consider the example where the Landslide Hazard membership 

function maxima for “low,” “medium,” and “high” are associated with domain values of 0, 50, and 

100, respectively. Using truth values assumed in the examples above (low/0.0, medium/0.3,

high/1.0 ), the defuzzified value using the CoM method would be:

!

0" 0.0 + 50" 0.3 + 100 " 1.0

0.0 + 0.3 + 1.0

= 88.5

2.1.7 Example Computation of a Simple Fuzzy System

Presenting a complete graphical example for a simple fuzzy IF-THEN rule-block can summarize

the elements of fuzzy logic systems. Figure 4 presents the fuzzy rule-block of Table 1, showing

the membership functions for each variable as assumed above. Consider a case where the input

data into the fuzzy logic system is Slope = 35 degrees, Distance = 27km, Lithology is landslide

deposit with a truth value of 0.5. Using BSUM to aggregate the same output values and CoM

for defuzzification, a final crisp landslide hazard value of 88.5 is computed.
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Figure 4. Complete example of a 4-variable, 4-rule fuzzy system (Table 1) for an input data scenario of Slope = 35 degrees,

Distance = 27km, and Lithology IS landslide deposit with a truth value of 0.5.
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2.2 Design of Fuzzy Logic Systems

Assembling the elements of fuzzy logic systems, described above, to express a solution of

a problem requires a significant design effort. The general design phases for developing CAMEL

were based on the design guide of Berkan and Trubatch (1997). The basic design phases consist

of (1) knowledge elicitation, (2) system design, and (3) implementation.

2.2.1 Knowledge Elicitation

Knowledge elicitation is the process of extracting useful and reproducible knowledge from

experts, literature sources, quantitative data, and other models. Knowledge elicitation from

experts requires direct observation of experts (e.g., a doctor making a diagnosis), face-to-face

interviews, or the use of questionnaires. To extract knowledge from scientific literature, the text

can be analyzed to identify the variables involved and their respective categories, as well as to 

pull out logical propositions. One sentence may yield several rules or several sentences might

lead to a single rule. Descriptions of consequences or actions can be used to identify output

variables, while inputs are often indicated by the descriptions of causes. Within scientific

literature, much of the knowledge might be represented in graphical form, which might be

possible to extract rules in a similar way as with empirical data. Otherwise, scientific charts and 

figures can be used to identify outputs, inputs, and their association. Direct translation of text

can be difficult, if not impossible. Knowledge elicitation from empirical data involves extracting

IF-THEN rules from a data set, either manually or automatically with the assistance of

appropriate methods (e.g., artificial neural networks). Mathematical equations can be plotted and 

used to identify rules. The essence of fuzzy rule extraction is to identify clusters of data points

that indicate an association with desired categories and determine the relationships between the

categories of each variable. Conceptually, this process is similar to fitting a line to a scatterplot

of data.

2.2.2 System Design

The model design phase serves to formalize the results of knowledge elicitation through 

various design tasks. Typically these tasks consist of (1) design of variables, (2) rule

composition, and (3) and selecting the numerical method for translating inputs to ouputs (rule
13

implication). These tasks must be performed in sequence, but the design process of course

requires iteration.

2.2.2.1 Variable Definition

The first task for the design phase is the definition of variables. There are two general

approaches to variable design: data-driven design and semantic design. The two approaches are

opposite means of designing the same elements. The three elements for each variable in a fuzzy

logic system include (1) the numeric domain in which the variable is valid, (2) membership

functions, and (3) linguistic labels for each membership function. The primary goal of the

variable design process is to determine the number of membership functions and their location in 

the domain. The difference between the data-driven and semantic design approaches is in the

specification of membership functions. Data-driven design utilizes data that represents the

desired system behavior or process. Semantic design uses heuristics or expert judgment.

Semantic design is necessary when there is a lack of appropriate quantitative data to meet

specific design criteria. Thus, the number of membership functions might correspond with the

number of data clusters discovered or the number of heuristics that can be articulated for each

variable. As with all design problems, iterations are often required and so data-driven and

semantic design are not mutually exclusive in practice.

With the number and location specified, the shape of each membership function needs to 

be determined. In most cases, simple piece-wise linear functions (e.g., triangular or trapezoidal)

are sufficient. Other shapes may be used to meet certain design criteria (e.g., how quickly or

slowly the model transitions from one state to another) or to subsequently fine-tune the model,

often with automated learning methods. Semantically appropriate labels should be selected for

each membership function. Label selection is one of the essential elements for making fuzzy logic

rules comprehendible.

2.2.2.2 Rule Composition

The next task is rule composition. In composing IF-THEN rules, individual statements

(e.g., “Slope Angle IS steep”) are combined using operators like AND and OR. A commonly 

used operator related to AND is the PROD operator, which is formulated as the product of two 
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truth-values. Higher input truth values are required for PROD to yield the same output truth 

value as computed using AND. In practice, the OR operator may not be necessary because a

single two-input rule using the OR operator is equivalent to two one-input rules when using

CoM defuzzification (Li, 2002). Avoiding use of the OR operator or, more strictly, using singleinput rules has three critical advantages. First, the task of specifying rules for every input

combination is avoided (Combs and Andrews, 1998). Second, computational performance of the

system is improved (Lazzerini, 2000). Third, the resulting rule-block is easier to read and

potentially understand (Guillaume, 2001).

After determining what fuzzy operator to employ to relate input variables, rule

composition is completed by determining an appropriate strategy for organizing the rules.

Examples of strategies include the following: competitive rules, cooperative rules, weighted rules,

prioritized rules, and hierarchical rules. Each strategy (or some combination thereof) can be used 

in modeling a process; the choice of strategy depends on the particular design objectives (e.g.,

performance with missing input data). Competitive rules are rules that consider the same input

variables (though likely different fuzzy values) to represent the same output variable.

Cooperative rules, conversely, make use of different variables to represent the same output

variable. Weighted rules express varying confidence or importance across some set of rules, using

degree of support (DoS) weights. Rule prioritization refers to intentional sequencing of

computing rules. The most common means of rule prioritization is the creation of a series of

rule-blocks in which outputs from one rule-block are used as input in another.

2.2.2.3 Rule Implication

The final task of design is rule implication. Implication refers to the design elements that

compute outputs from inputs statements. Rule implication design requires two decisions. A

choice must be made regarding the means of combining different truth-values determined for the

same output fuzzy values using either MAX or BSUM. MAX uses only the results of a single

rule, while BSUM takes a bounded sum, not to exceed 1, of all rule truth-values. In contrast to 

MAX, BSUM potentially takes into account multiple rules. As a result, BSUM will always

yield an equal or higher combined truth-value than MAX. Finally, the choice must be made
15

regarding whether to defuzzify results and, if so, how.

3 CAMEL Knowledge Elicitation

The first step in the development of CAMEL was knowledge elicitation, which forms the

basis for variable definition and composition of IF-THEN rules. For initial development, two 

sources of knowledge were used: scientific literature and expert judgment. This knowledge can be

revised or supplemented in the future using empirical data, outputs from alternative models, or

knowledge elicited from experts. Knowledge elicitation was done by compiling literature

describing analyses of past landslide-triggering earthquakes and extracting relevant numerical or

correlative information. Expert judgment was used to fill any gaps in knowledge from the

literature. The primary literature sources used for knowledge elicitation included Keefer (2002),

Liao et al. (2002), Hancox et al. (2002), Parise and Jibson, (2000), Bommer and Rodriguez

(2000), Keefer (2000), Keefer (1999), Rodriguez et al., (1999), Keefer (1993), and Keefer (1984).

At this point in development, empirical data was not used as a source for knowledge

elicitation, for three reasons. First, in general there are few landslide inventories (and fewer

spatial databases) from historical earthquakes. Second, many of the available inventories do not

provide detailed information, for example, with respect to different types of landslides. Lastly,

for initial development of CAMEL it is useful to be able to use empirical data to help evaluate

the quality of the foundational sources of knowledge. Mathematical equations, such as the many

available equations for calculating Newmark displacement, were not used for initial development

for two reasons. There do not exist mathematical models for each category and type of

earthquake-induced landslides. Most critically, use of existing equations would constrain the

choice of input and output variables. Further, few mathematical relationships exist that predict

landslide concentration, especially for particular types of landslides. With the conceptual and 

algorithmic framework of CAMEL developed, empirical data can be used in the future to refine

the variable definitions, individual rules or rule blocks, and rule weights.

The first step in the knowledge elicitation process was determination of input and output

variables. The output variables were largely determined by the design objectives for CAMEL

(i.e., prediction of landslide concentration for every landslide type). For initial development, we
16

decided to reduce the number of output variables by aggregating similar landslide types based on 

the classification of Keefer (1984). Thus, ten distinct types are represented by the six output

variables listed in Table 2. For this stage in the research, soil lateral spreads are not considered

because of the significantly different type of data and knowledge needed to characterize this

landslide type. An initial list of input variables was obtained by analyzing a cross-section of the

landslide and earthquake ground-motion literature using text indexing and search software to help

identify commonly sited indicators, conditioning factors, and independent variables. The

potential list of input variables was pared down based on the specificity of information within 

the literature for each potential variable and by combining similar concepts into single variables.

The input variables of CAMEL are specified and discussed later in the report.

Table 2. Aggregated types of earthquake-induced landslides, listed by category, modeled by 

CAMEL. Taxonomy and terminology after Keefer (1984).

• Category I

• Disrupted rock falls and slides

• Rock avalanches

• Disrupted soil slides, falls, and avalanches

• Category II

• Rock slumps and block slides

• Soil slumps and block slides

• Category III*

• Rapid soil flows

*Soil lateral spreads not considered

Examination of the summative literature revealed that there are two general types of

knowledge with respect to past occurrence of earthquake-induced landslides. The first type of

knowledge describes indicators or geologic conditions that suggest whether the occurrence of a

particular type of landslide is possible. The second type of knowledge describes the variation in

intensity of hazard with the increasing magnitude of particular variables. These two types of

knowledge––indicators and intensifiers––serve as the organizing principle for the design of

CAMEL. The specific knowledge and its role in the design of CAMEL are discussed in the

following section specifying the fuzzy system elements of CAMEL.
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4 CAMEL Design and Specification

CAMEL is comprised of two modules, each made up of several IF-THEN rule-blocks to 

respectively determine the possibility and areal concentration of earthquake-induced landslides

(Figure 5). The possibility module determines whether the occurrence of each respective

landslide type is possible. The hazard module determines the relative hazard, expressed in units

of landslides per square kilometer, for each possible landslide type. This modular framework 

serves to separate the implementation of the knowledge about possibility of occurrence from

knowledge about relative hazard for each landslide type listed in Table 2.

Figure 5. Two-module framework of CAMEL, including the possibility and hazard

modules.

The two modules of CAMEL are described in detail below. The conceptual definition of

each variable is described, in addition to the sources of knowledge. The input variables are

defined graphically by presenting each variables set of membership functions, specified across a

particular domain. Output variable definitions are presented in table form. The composition of

each respective block of IF-THEN rules is described. Design choices that were made in

translating elicited knowledge to the elements of CAMEL are discussed. Finally, any limitations

of the particular design element are presented.
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4.1 Possibility Module

The structure and data flow of the possibility module is given in Figure 6. The module

consists of seven input variables, together with seven corresponding rule blocks that are

organized in series––a competitive arrangement. Each rule block contains rules associated with a

specific variable to collectively determine the degree to which each landslide type is possible. If

one indicator variable specifies that a particular landslide type is not possible, the overall result

of this chain of rule-blocks will be that the particular landslide type is impossible. If all indicator

variables show that a landslide type is possible, the minimum truth-value across these rule blocks

is the output truth-value for the possibility module. The magnitude of the output truth-value

indicates the degree to which the user-supplied input data suggests that each landslide type is

possible based on the knowledge represented by the IF-THEN rules of the module. The AND

operator is used for all rule blocks in the possibility module. PROD would result in lower

computed possibilities because truth-values are combined through multiplication. This design

choice means that CAMEL assumes landslide types are possible in the face of uncertainty.

Lastly, all rules in the possibility module have an equal DoS weight of 1.0.

The possibility of each landslide type is expressed through the output variable

LandslideType. LandslideType is a nominal variable (i.e., a crisp value cannot be determined

through defuzzification) comprised of six categories corresponding to each landslide type

modeled by CAMEL. Outputs associated with this variable are a fuzzy set with a truth-value

computed for each of the six landslide types. If a truth-value is zero (0) for a particular landslide

type, that type is considered to be impossible based on the inputs provided to CAMEL.

Positive possibility values then indicate that the particular landslide type is possible to the

degree indicated.
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Figure 6. Possibility module of CAMEL.

Table 3 enumerates the indicator variables that comprise the inputs of the possibility

module of CAMEL. The variables are listed in the sequence in which CAMEL considers each

indicator. The specific sequence is arbitrary. Each indicator is briefly defined with the

quantitative units, the domain, and the number and label of the fuzzy values for each variable.

Variables that have a domain defined by a minimum value of -1 have an actual minimum of 0.

The negative value is a design technique to track whether the user has not provided an input

value. For these variables, “missing” (data) is a valid data value (i.e., data does not have to be

specified for the particular CAMEL variable to compute outputs). The design and specification 

of each indicator variable and associated rule block is described in detail below in the order listed 

in Table 3.
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Table 3. CAMEL Possibility Module Input Variables.

Variable Name Units Min Max Fuzzy Value Labels

pTerrainRough Slope of slope

angle

0 40 planar

rough

pShakeIntensity

Shakemap

intensity

(MMI)

-1 12

missing

greater_than_7

greater_than_8

greater_than_9

pSoilDepth Meters -1 10

missing

shallow

deep

pSlopeHeight Meters -1 300

missing

low

high

pMaterialType (linguistic) - -

missing

rock

soil

pMoisture Percent 0 100 more_than_moist

about_saturated

pSlopeAngle

Degrees from 

the horizontal 0 90

between_5and40

between_15and40

greater_than5

greater_than_15

greater_than_25

greater_than_35

4.1.1 pTerrainRough

Quantitative knowledge about slope curvature and earthquake-induced landslide hazard is

relatively sparse. A study by Liao et al. (2002) found that a variable they referred to as “terrain 

roughness” correlated exceptionally well with the occurrence of landslides from the 1999 Chi-Chi,

Taiwan earthquake. Landslides triggered during this event were primarily disrupted falls and 

slides. Terrain roughness is simply the unsigned slope curvature in profile. In other words it is

the second derivative of elevation in the down-slope direction, with all calculation done in

degrees. The two fuzzy values for pTerrainRough––“planar” and “rough”––lie on either side of

a second-derivative value of 15 (Figure 7). Liao et al. (2002) found a significant increase in

landslide concentration above this value. Liao et al. (2002) observed that there is some small

possibility of having a landslide at terrain roughness values of as low as 5. An s-shape

membership function is used rather than a straight-line curve because it results in very low truthvalues near a pTerrainRough of zero. No accommodation for missing data is made for
21

pTerrainRough because slope curvature can be calculated from elevation data, which is likely to 

be used to calculate regional slope angle values (a requirement for running CAMEL).

Figure 7. Membership functions for pTerrainRough.

The rules that determine possibility based on pTerrainRough are listed in Table 4. For

Category I landslides, possibility is true only if pTerrainRough is “rough.” It is assumed that

terrain roughness is not a significant indicator for the remaining landslide types of CAMEL, and 

so possibility is true for either fuzzy value. This rule block is currently first in the sequence of

the possibility module. Therefore, it has to consider all input combinations. Even though it is

not important whether, for example, soil slumps have a “rough” pTerrainRough, this fuzzy value

must be checked for.

Table 4. Possibility module rule block expressing knowledge about pTerrainRough.

IF THEN

pTerrainRough DoS LandslideType7

rough 1.00 rock_fall

rough 1.00 rock_avalanche

rough 1.00 soil_slide

rough 1.00 rock_slump

rough 1.00 soil_slump

rough 1.00 soil_flow

planar 1.00 soil_slide

planar 1.00 rock_slump

planar 1.00 soil_slump

planar 1.00 soil_flow
22

4.1.2 pShakeIntensity

The minimum required shaking intensity required for each respective type of landslide is

represented by the indicator pShakeIntensity. The units of the variable are based on the

ShakeMap intensity scale (Wald et al., 1999), which is similar to the Modified Mercalli Intensity

(MMI) scale, but is a ratio scale. This facilitates use of ShakeMap outputs, which are available

in GIS data formats from actual or scenario events (http://earthquake.usgs.gov/shakemap/). As

illustrated in Figure 8, pShakeIntensity is made up of three fuzzy values: “missing”,

“greater_than_7”, “greater_than_8”, and “greater_than_9.” The lowest value is applied to 

Category I landslides, not including rock avalanches. The middle value is applied to Category II

(coherent slides) and Category III landslides (rapid soil flows). The highest value is related to the

occurrence of rock avalanches. MMI threshold values for rock avalanche were determined from

minimum magnitude and distance information in Hancox et al. (2002) and Keefer (1984), which 

were then converted to MMI. The other values are based on Rodriguez et al. (1999) and Keefer

(1984) (Figure 9). Each membership function increases from a truth-value of zero (0) at the

lowest recorded minimum MMI threshold to a truth of one (1) at the maximum-recorded

minimum MMI value. If no intensity data is provided, CAMEL will compute static

susceptibility for each landslide type.

Figure 8. Membership functions for pShakeIntensity.
23

Figure 9. Minimum shaking intensity required for occurrence of different categories of

landslides (Rodriguez et al., 1999). Gray boxes from Keefer (1984); white boxes from

Rodgriguez et al. (1999).

The rules to determine possibility for each landslide type based on pShakeIntensity are
24

listed in Table 5. LandslideType7 is used to link this rule block with the previous rule block. As

described above, AND is used to pass on the truth-value from the previous rule block. The last

six rules of the rule block handle cases of missing data (to conduct static susceptibility analysis).

Table 5. Possibility module rule block expressing knowledge about pShakeIntensity.

IF THEN

LandslideType7 pShakeIntensity DoS LandslideType6

rock_fall greater_than_7 1.00 rock_fall

soil_slide greater_than_7 1.00 soil_slide

rock_slump greater_than_8 1.00 rock_slump

soil_slump greater_than_8 1.00 soil_slump

soil_flow greater_than_8 1.00 soil_flow

rock_avalanche greater_than_9 1.00 rock_avalanche

rock_fall missing 1.00 rock_fall

rock_avalanche missing 1.00 rock_avalanche

soil_slide missing 1.00 soil_slide

rock_slump missing 1.00 rock_slump

soil_slump missing 1.00 soil_slump

soil_flow missing 1.00 soil_flow

4.1.3 pSoilDepth

The variable pSoilDepth is based on knowledge from Keefer (1984) and Bommer and 

Rodriguez (2000). The two fuzzy values lie on either side of 3 meters (Figure 10). This fuzzy

threshold represents knowledge regarding whether there is enough soil for a deep-seated failure to 

occur. If data regarding slope depth is missing CAMEL will assume that Category I landslides in 

soil are possible. Table 6 shows the associated rules. Notice that the truth-values for all of the

fuzzy values are simply passed on to the next rule block, except for ”soil_slump.”.

Figure 10. Membership function for pSoilDepth.
25

Table 6. Possibility module rule block expressing knowledge about pSoilDepth.

IF THEN

LandslideType6 pSoilDepth DoS LandslideType5

soil_slump deep 1.00 soil_slump

soil_slump missing 1.00 soil_slump

rock_fall 1.00 rock_fall

rock_avalanche 1.00 rock_avalanche

soil_slide 1.00 soil_slide

rock_slump 1.00 rock_slump

soil_flow 1.00 soil_flow

4.1.4 pSlopeHeight

Keefer (1984) observed that for the occurrence of rock avalanches the local slope height

must be greater than or equal to 150 meters. This knowledge is reflected in the variable

pSlopeHeight (Figure 11), which is comprised of two fuzzy values, “low” and “high.” The latter

value is defined so that the truth-value is one (1) at a slope height value of 150 meters. The other

membership function is simply inverse. The steepness of the membership functions is warranted 

because 150 meters is an extreme minimum for the avalanche dataset of Keefer (1993). A fuzzy

value of “missing” is also possible and is used to assume that rock avalanches are possible in the

event of missing slope height data values. Table 7 lists the associated rules and shows that

calculations are only performed on the fuzzy value “rock_avalanche.” Otherwise, truth values

associated with other landslide types are passed to the next rule block.

Figure 11. Membership functions for pSlopeHeight.
26

Table 7. Possibility module rule block expressing knowledge about pSlopeHeight.

IF THEN

LandslideType5 pSlopeHeight DoS LandslideType4

rock_fall 1.00 rock_fall

rock_avalanche high 1.00 rock_avalanche

soil_slide 1.00 soil_slide

rock_slump 1.00 rock_slump

soil_slump 1.00 soil_slump

soil_flow 1.00 soil_flow

rock_avalanche missing 1.00 rock_avalanche

4.1.5 pMaterialType

pMaterialType is a nominal variable. It can take on three nominal values; “rock”, “soil”,

and “missing.” The variable serves as a binary switch to indicate whether material properties

inputs provided (i.e., hGroundClass, defined below) refer to rock or soil. If no indication is

provided, the fuzzy system, as shown in the rule block of Table 8, will apply the same material

properties to both rock and soil landslides. Further, both rock and soil landslides are assumed 

possible.

Table 8. Possibility module rule block expressing knowledge about pMaterialType.

IF THEN

LandslideType4 pMaterialType DoS LandslideType3

rock_fall rock 1.00 rock_fall

rock_avalanche rock 1.00 rock_avalanche

soil_slide soil 1.00 soil_slide

rock_slump rock 1.00 rock_slump

soil_slump soil 1.00 soil_slump

soil_flow soil 1.00 soil_flow

rock_fall missing 1.00 rock_fall

rock_avalanche missing 1.00 rock_avalanche

soil_slide missing 1.00 soil_slide

rock_slump missing 1.00 rock_slump

soil_slump missing 1.00 soil_slump

soil_flow missing 1.00 soil_flow

4.1.6 pMoisture

pMoisture represents knowledge about different categories of landslides with respect to 
27

ground moisture content or water table depth. The variable describes the degree to which the

analysis layer is saturated, relative to the depth of the layer. pMoisture is conceptually the ratio 

of the ground water height over the depth of the ground layer under analysis, multiplied by

100%. This is the same definition as the m-value used in infinite slope models typically applied 

for regional modeling (e.g., Jibson et al., 1998). The knowledge for this variable is based on 

Keefer (1984). The definition of this variable is shown in Figure 12. The lowest value,

“more_than_moist”, is related to Category II landslides, assuming that the occurrence of coherent

slides require some ground water. The membership function shape indicates that above 0 percent

layer saturation some possibility exists. The high value, “about_saturated”, is related to rapid 

soil flows, which require high moisture content (Keefer, 1984). A threshold value of 75%

(indicating truth value of 1) was assumed as a conservative value describing that the entire layer

saturated. A higher threshold would reduce possibilities assigned to less-saturated slopes.

Looking at the corresponding rules listed in Table 9, notice that “rock_fall”, “rock_avalanche”,

and “soil_slide” are possible regardless of moisture conditions (Keefer, 1993; Keefer, 1984).

Figure 12. Membership functions of pMoisture.
28

Table 9. Possibility module rule block expressing knowledge about pMoisture.

IF THEN

LandslideType3 pMoisture DoS LandslideType2

rock_fall 1.00 rock_fall

rock_avalanche 1.00 rock_avalanche

soil_slide 1.00 soil_slide

rock_slump more_than_moist 1.00 rock_slump

soil_slump more_than_moist 1.00 soil_slump

soil_flow about_saturated 1.00 soil_flow

4.1.7 pSlopeAngle

Knowledge describing the minimum slope angle required for the occurrence of each

landslide type is represented by the variable pSlopeAngle. The five membership functions,

shown in Figure 13, are based on Hancox et al. (2002), Rodriguez et al. (1999), and Keefer (1984)

(Table 10). In defining the membership function for rapid soil flows, we rounded up the

(highest) minimum value found in the literature from 2.3 degrees to 5. The majority of flows in 

the data set of Keefer (1984) are greater than 5 degrees. This simplifies definition of pSlopeAngle

membership functions. Other minimum values attenuate from a truth-value of one (1) at the

lowest published minimum slope angle to a truth-value of zero (0) in 2.5 degrees. The fuzzy

value “greater_than15” is applied to Category I landslides in soil and is based on the published 

values for disrupted soil slides. This means that the possibility for soil avalanches is currently 

over-predicted by CAMEL because the general minimum slope angle for this landslide type is 35 

degrees. Disrupted soil slides are very common (Keefer, 1984); so the variable is designed to 

reflect this priority. The fuzzy value “greater_than35” is defined based on knowledge about

disrupted rock falls and slides. Knowledge about soil block slides was used to define the fuzzy

value “between_5and40” and is applied to both soil block slides and soil slumps. The fuzzy

value “between_15and40” is based on knowledge about all coherent rock slides. The slope angle

maximum of 40 degrees for the Category II landslides is based on Hansen and Franks (1991),

Cruden and Varnes (1996), and the data set of Keefer (1984). Because of uncertainty in this

maximum, the membership function attenuates slowly until it reaches a truth-value of zero (0.0)

at 90 degrees. Table 11 shows the rules dealing with each fuzzy value of pSlopeAngle.
29

Figure 13. Membership functions for pSlopeAngle.

Table 10. Knowledge about minimum slope angles required for particular types of

landslides.

Landslide Type Minimum Slope Angle (degrees)

Keefer (1984) Rodriguez et

al. (1999)

Hancox et al.

(2002)

Disrupted rock falls and rock slides 35-40 35 40

Disrupted soil slides/soil falls 15/40 55 25-35

Rock avalanches 25 n/a 25-35

Rock slumps and rock block slides 15 15 15

Soil slumps and soil block slides 5-10 8 15

Rapid soil flows 2.3 0 2

Table 11. Possibility module rule block expressing knowledge about pSlopeAngle.

IF THEN

LandslideType2 pSlopeAngle DoS LandslideType

rock_fall greater_than_35 1.00 rock_fall

rock_avalanche greater_than_25 1.00 rock_avalanche

soil_slide greater_than_15 1.00 soil_slide

rock_slump between_15and40 1.00 rock_slump

soil_slump between_5and40 1.00 soil_slump

soil_flow greater_than5 1.00 soil_flow

4.2 Hazard Module

The hazard module is illustrated in Figure 14. The module treats each landslide type
30

separately in two sub-modules, static susceptibility and seismic hazard to compute different

ranges of areal concentration values for each of the six landslide types in CAMEL. After a

general introduction of the module below, the three main sets of elements – input variables,

output variables, and rule blocks – are specified in detail in the remainder of the section.

Figure 14. Hazard module of CAMEL.

The susceptibility sub-module is comprised of knowledge about variables independent of

earthquake shaking (i.e., static conditions). This knowledge about static susceptibility is

represented with two rule blocks. In the first rule block, output variable values monotonically 

increase with increasing input variable values. In other words, the rules express that for every

input increase of one fuzzy value (e.g., “low” to “medium”), the output increases by one fuzzy

value. The second rule block of the susceptibility sub-module considers less important variables

(or variables associated with poorer detail or quality knowledge), referred to as modifiers in 

CAMEL. Modifiers contribute to static susceptibility by increasing or decreasing the

susceptibility by no more than one fuzzy value, or not at all. For example, if a slope is near an 
31

undercutting stream and would otherwise have a “medium” static susceptibility, the computed 

static susceptibility might be “high.”

The seismic hazard sub-module considers the earthquake ground-shaking with respect to 

the static susceptibility computed by the previous sub-module. The seismic hazard sub-module

considers all possible combinations of static susceptibility and shaking intensity fuzzy values.

The output of the seismic hazard sub-module (and the overall output of CAMEL) is an

estimation of areal concentration (landslides per square kilometer) for each landslide type.

Importantly, as illustrated in Figure 14, the possible range of concentration values is different for

each landslide type. This is described in further detail below.

4.2.1 Hazard Module Input Variables

The hazard module is generally, but not directly, based on quantitative knowledge from

the scientific literature. Each domain and some specific membership functions are specifically 

based on elicited knowledge, while other elements are based on design criteria or judgment.

Except for specific reasons, membership functions are defined using triangular shapes. In lieu of

quantitative data, membership functions were defined using three constraints. First, the domain 

must be completely specified (or “covered”) with membership functions. Second, all neighboring

membership functions must intersect at a truth-value of 0.5. Third, neighboring membership

functions should mirror each other. For example, if one membership function exhibits a truthvalue of 0.7 at a particular domain location, the neighboring membership function should give a

value of 0.3 at the same location. These criteria help ensure the highest truth-value possible

when inputs are uncertain or vague. The criteria further provide that there are no input data in

which CAMEL will not compute outputs and prevents dramatically different results over small

changes in values.

Table 12 lists all of the input variables for the hazard module. Each variable is defined 

with the quantitative units, the domain (minimum and maximum values), and the label of the

fuzzy values for each variable. Each input variable is specified in further detail below, including

utilized sources of knowledge.
32

Table 12. CAMEL input variables – variables and modifiers.

Variable Name Unit Min Max

Fuzzy Value

Labels

hDisturbanceDist Meters 0 200 close

far

hGroundClass

Relative 

(ratio) units

1 5

very_good

good

moderate

poor

very_poor

hMoisture Percent 0 100

very_low

low

medium

high

very_high

not_very_low

hShakeIntensity

Shakemap

MMI 4 10

light

moderate

strong

very_strong

severe

violent

extreme

hSlopeAngle

Degrees

from the 

horizontal

5 65

very_gradual

gradual

medium_gradual

medium

medium_steep

steep

very_steep

extremely_steep

nearl_vertical

hVegetation Percent 0 100 sparse

dense

4.2.1.1 hDisturbanceDist

Conceptually, hDisturbanceDist facilitates representation of morphological factors

contributing to slope failure, such as toe erosion by an undercutting stream or an over-steepened 

road cut (Cruden and Varnes, 1998). Specifically, it is the distance in meters between the slope

(however defined) and the identified linear disturbance feature. The domain of the variable and

number of fuzzy values is assumed based on inspection of the Loma Prieta landslide inventory of

Keefer (1998). Membership function definitions, shown in Figure 15, for the two fuzzy values

are based on an assumption that the influence of some linear disturbance is significantly less
33

beyond a distance of 100 meters. The s-shape of the membership functions was chosen so that

transition between close and far is faster than equivalent straight-line curves. In other words, the

fuzziness between the two values is less in comparison to triangular membership functions

towards the ends of the domain.

Figure 15. Membership functions of DisturbanceDist.

4.2.1.2 hGroundClass

The variable hGroundClass is based on a combination of terminology and knowledge

from Hancox et al. (2002) and Keefer (2000) about the relationships between qualitative

descriptions of slope material or lithology with earthquake-induced landslide hazard. Figure 16

presents the knowledge from Keefer (2000), describing a monotonic correlation between five

linguistic descriptions of lithology that represents a decreasing quality of cementation, induration,

or weathering with increasing concentrations of landslides. The ground classes of Hancox et al.

(2002) are listed in Table 13. hGroundClass is designed as an index of ground conditions and 

material properties, such as shear strength and rock jointing, with lower values indicating poorer

ground conditions (Figure 17). For this reason, membership function definition was a simple

matter of creating identical function shapes for each ground class value. Specifying input data for

hGroundClass requires that the user translate data and/or knowledge regarding the ground

conditions into one of five categories. This variable represents either rock or soil conditions

depending on the value for pMaterialType.
34

Figure 16. Knowledge describing increasing likelihood of landslides with respect to quality 

of rock cementation or induration from a study of landslides caused by the 1989 Loma

Prieta earthquake (Keefer, 2000).
35

Table 13. The ground classes of Hancox et al. (2002).
36

Figure 17. Membership functions of hGroundClass.

4.2.1.3 hMoisture

Conceptually, hMoisture is defined in the same manner as pMoisture. The variable

describes the degree to which the analysis ground layer is saturated, relative to the depth of the

layer. hMoisture is the ratio of the ground water height over the depth of the ground layer under

analysis, multiplied by 100%. The domain is from 0 to 100% layer saturation. Just as

hGroundClass, the five fuzzy values for hMoisture were determined by dividing the domain into 

five categories defined by symmetrically shaped membership functions (Figure 18).

Figure 18. Membership functions of hMoisture.

4.2.1.4 hShakeIntensity

The variable hShakeIntensity represents knowledge about earthquake shaking intensity.

The variable is simply a fuzzy translation of the MMI scale (considering the values between and 

including 4 and 10), with a symmetrical membership function located at each MMI value (Figure
37

19). The fuzzy value labels are based on the ShakeMap terms of Wald et al. (1999). No fuzzy

values greater than 10 are defined because ShakeMap does not provide these values and little data

exists on landslides related to such large intensities.

Figure 19. Membership functions of hShakeIntensity.

4.2.1.5 hSlopeAngle

Knowledge about the influence of increasing slope angle is represented by hSlopeAngle

(Figure 20). The units are degrees from the horizontal––the same as pSlopeAngle. The minimum

value (5 degrees) defining the domain is based on the indicator threshold for rapid soil flows and 

soil slumps. For slope values less than 5 degrees, no landslide type is possible with respect to 

the current specifications of CAMEL. A maximum value of 65 degrees is used because this value

typically served as the maximum value analyzed in studies refereed to for knowledge elicitation.

For example, Hancox et al. (2002) relates a cutoff 65 degrees with “High to Very High”

susceptibility. To be logically consistent, each membership function defining pSlopeAngle has a

corresponding membership function in hSlopeAngle. The membership function located at 20 

degrees is to provide five functions between 15 and 40, corresponding to the possible slope

values for rock slumps and block slides. Membership functions located at angles greater than 40 

are spaced at equal intervals up to 65 degrees. The asymmetric triangle-shaped membership 

functions provide smooth transitions between fuzzy values. If symmetrical trapezoid shapes

were used to define hSlopeAngle, output values for CAMEL would be the same for some ranges

of slope angles because a trapezoidal shape defines a truth value of one (1) for an interval rather

than a point.
38

Figure 20. Membership functions of hSlopeAngle.

4.2.1.6 hVegetation

Little specific knowledge about the influence of vegetation on the different types of

earthquake-induced landslides exists in the literature beyond the decision tree for assessing

disrupted rock fall and slide susceptibility of Keefer (1993). For this reason, a simple definition 

for hVegetation was assumed (Figure 21). Conceptually, the variable refers to the vegetation 

coverage (density) of a given area, expressed as a percent of the area. Two fuzzy values,

“sparse” and “dense” are defined, with 50 percent serving as the decision boundary. The utility

of this variable may be limited in the current version of the model, but does not degrade the

performance of the model because it is an optional variable.

Figure 21. Membership functions of hVegetation.
39

4.2.2 Output Variables – Landslide Concentration

The variables listed in Table 14 are the outputs associated with the hazard module of

CAMEL. Each of the six landslide types is associated with four output variables. These

correspond to two seismic hazard output variables––one that is defuzzified and a nominal

variables that isn’t (e.g., RAvSeismic and RAvSeismicFz)––and, similarly, two static

susceptibility output variables (e.g., RAvStatic and RAvStaticFz). (FuzzyTechTM, the software

used to implement CAMEL, requires specification of the defuzzification method as part of

defining variables and so two separate variables must be defined if one wants to view outputs

both as a defuzzified value and as fuzzy sets.) The fuzzy values for all of the output variables

are the same, but are associated with a different domain and membership function locations (latter

three columns of Table 14). Definition of the domains and membership locations are described 

in more detail below.
40

Table 14. Specifications of output variables for the hazard module.

Variable Name

Fuzzy Value

Labels

Location of 

Membership

Function 

Centers

(ln ls/km

2

)

Location of 

Membership

Function 

Centers

(ls/km

2

)

RAvSeismic

RAvStatic 

RAvSeismicFz

RAvStaticFz

very_low

low

medium

high

very_high

-4.605

3.454

-2.303

-1.151

0

0.01

0.0315

0.1

0.316

1

RFallSeismic

RFallStatic 

RFallSeismicFz

RFallStaticFz

very_low

low

medium

high

very_high

-2.303

-0.576

1.151

2.878

4.605

0.1

0.562

3.161

17.779

100

SFallSeismic

SFallStatic 

SFallSeismicFz

SFallStaticFz

very_low

low

medium

high

very_high

-2.303

-0.576

1.151

2.878

4.605

0.1

0.562

3.161

17.779

100

RSlumpSeismic

RSlumpStatic 

RSlumpSeismicFz

RSlumpStaticFz

very_low

low

medium

high

very_high

-2.303

-1.151

0

1.151

2.303

0.1

0.316

1

3.161

10

SSlumpSeismic

SSlumpStatic 

SSlumpSeismicFz

SSlumpStaticFz

very_low

low

medium

high

very_high

-2.303

-1.151

0

1.151

2.303

0.1

0.316

1

3.161

10

SFlowSeismic

SFlowStatic 

SFlowSeismicFz

SFlowStaticFz

very_low

low

medium

high

very_high

-2.303

-1.151

0

1.151

2.303

0.1

0.316

1

3.161

10

Because the best knowledge available is for disrupted rock falls and slides, the domain and 

membership functions for this type of landslide were defined based on the quantitative

knowledge available and then scaled down to describe the other landslide types. For disrupted 

rock fall and slides, the maximum possible rock fall concentration is defined as 100 landslides per

square kilometer (ls/km2) and the minimum at 0.1 ls/km2. This maximum is an assumed value

based on the high concentrations (about 74 ls/km2) measured by Parise and Jibson (2000) for the

1994 Northridge, CA earthquake. The highest category in the rock fall decision tree of Keefer
41

(1993) is only 16 ls/km2.

A natural log scale was used for the domain of all output variables of the hazard module

for two reasons. First, an arithmetic scale would result in the lower membership functions being

clustered tightly together, giving too much influence to the higher fuzzy values during the

defuzzification process. With a natural log scale, the peak of each membership function can be

located at equal intervals. This leads to the second reason for the log scale, being that the 3.16 

ls/km2 geometric increase between each fuzzy value is similar to the four-fold increase between 

the concentration categories of Keefer (1993). The use of a log scale creates the problem that

landslide concentration output values will always be greater than zero (0). However, from an 

implementation perspective, the variable LandslideType can be used to ensure that, if the

possibility is zero (0) (or some other very small value), the reported landslide concentration is

also zero (0).

In the latter three columns of Table 14, the fuzzy value labels are given with the

respective location within the domain of the membership function peaks, expressed both as a

natural log and arithmetically (ls/km2). CoM (Center of Maximum) defuzzification is used, which 

only requires specification of the peak (or center) of each membership function. Notice that all of

the peak locations are located at equal intervals on the logarithmic domain.

The remaining output variables were specified by scaling the variable definition of

RFallSeismic (etc.) based on the historical abundance observations in Keefer (1984) (Table 15).

Keefer (1984) noted roughly a magnitude difference between several groupings of landslide types,

with rock avalanches being least abundant and rock falls being most abundant. Because disrupted 

soil slides are very abundant, the corresponding variables (SFallSeismic etc.) were specified 

identically to the variables for rock falls and disrupted rock slides. Soil slumps and block slides

are abundant according to Keefer (1984), with total numbers being about a magnitude less than 

for rock falls and disrupted rock slides. Therefore, the variables defined for rock falls were scaled 

down by a factor of 10, making the maximum possible soil slump and block slide concentration 

10 ls/km2. According to Table 15 these landslide types are either a magnitude or two less

common than soil slumps and block slides, to simplify variable design, the same specification 

was given to rapid soil flows and rock slumps and block slides. Lastly, rock avalanches are three
42

magnitudes less common than rock falls and disrupted rock slides. However, to allow for a higher

maximum than indicated in Keefer, 1984, 1 ls/km2 is specified for the maximum possible rock

avalanche concentration.

Table 15. Relative abundance of earthquake-induced landslides from 40 historical,

worldwide earthquakes after Keefer (1984). Landslide types are listed in order of

decreasing total numbers.

Very abundant (>100,000)

Rock falls

Disrupted soil slides

Rock slides

Abundant (10,000 to 100,000)

Soil slumps

Soil block slides

Soil avalanches

Moderately common (1,000 to 10,000)

Soil falls

Rapid soil flows

Rock slumps

Uncommon (100 to 1,000)

Rock block slides

Rock avalanches

4.2.3 Hazard Module Rule Blocks

For each of the six landslide types in CAMEL, there are three associated rule blocks that

make up the respective component of the hazard module and sub-modules. Thus, there are a

total of 18 rule blocks in the hazard module – six static susceptibility rule blocks, six optional

“modifier” rule blocks, and six seismic hazard rule blocks. The variables considered within the

static susceptibility and modifier rule-blocks can be different between the respective landslide

types. The decisions of what role each variable plays with respect to the different landslide

types were made based on the results of knowledge elicitation. Table 16 lists each landslide type

considered in CAMEL together with which variables are treated as variables, modifiers or not at

all. pShakeIntensity is considered for all landslide types. For brevity, only the rule blocks for

disrupted rock slides and falls are presented below. Except for input variables considered, the

rule composition and design choices are the same. All of the rule blocks that comprise the hazard 

module of CAMEL are presented in Appendix A.
43

Table 16. Input variables considered for each landslide type within the hazard module of

CAMEL.

Landslide Type

Static 

Susceptibility Modifiers

Rock Avalanches hGroundClass

hSlopeAngle

hDisturbanceDist

Disrupted Rock Slides

and Falls

hGroundClass

hSlopeAngle

hDisturbanceDist

hMoisture

hVegetation

Disrupted Soil Slides,

Falls, and Avalanches

hGroundClass

hSlopeAngle

hDisturbanceDist

hMoisture

hVegetation

Rock Slumps and

Block Slides

hGroundClass

hSlopeAngle

hMoisture

hDisturbanceDist

hVegetation

Soil Slumps and Block

Slides

hGroundClass

hSlopeAngle

hMoisture

hDisturbanceDist

hVegetation

Rapid Soil Flows hGroundClass

hSlopeAngle

hDisturbanceDist

hMoisture

hVegetation

4.2.3.1 Static Susceptibility Rule Block

The static susceptibility rule-block for disrupted rock slides and rock falls is given in 

Table 17. Within the rule block, hGroundClass and hSlopeAngle, are considered with a separate

set of independent rules following the Combs and Andrews (1998) method for single-input,

single-output rule composition. That is, hGroundClass and hSlopeAngle are never considered 

together in a single rule. The Combs and Andrews (1998) method uses a union rule configuration 

(i.e., an implicit OR between rules) in place of the more common intersection rule configuration 

where every combination of fuzzy values between input variables is explicitly mapped using

AND.
44

Table 17. Static susceptibility rule block for disrupted rock slides and falls.

IF THEN

hGroundClass LandslideType hSlopeAngle DoS RFallStaticInt

very_good rock_fall 0.80 very_low

good rock_fall 0.80 low

moderate rock_fall 0.80 medium

poor rock_fall 0.80 high

very_poor rock_fall 0.80 very_high

rock_fall medium_steep 0.20 very_low

rock_fall steep 0.20 low

rock_fall very_steep 0.20 medium

rock_fall extremely_steep 0.20 high

rock_fall nearly_vertical 0.20 very_high

While the variables are treated separately, the variable LandlideType (from the possibility

module) is aggregated using the PROD operator with the particular susceptibility in all of the

rules. (PROD is used throughout the hazard module.) This is the element of the design that

passes on the determined possibility of each landslide type to the assessment of areal

concentration. If the possibility is zero (0), the concentration is also zero (0). If there is some

positive possibility, then the landslide concentration is also positive, although it may be very

low. An effect of this design is that computed landslide concentrations will be the same whether

the possibility is one (1) or less than one (1), but greater than zero (0). However, the lower

possibility value is expressed by the fuzzy output variables (e.g., RFallSeismicFz and

RFallStaticFz).

Combination of the output fuzzy values (i.e., based on hGroundClass and hSlopeAngle,

respectively) is done using the BSUM operator. Using BSUM facilitates different weighting of

the variables using a DoS weight. Currently in CAMEL, hGroundClass is weighted higher (DOS

= 0.8) than hSlopeAngle (DOS = 0.2). The weights chosen are arbitrary and can be modified in 

the future to fine-tune the performance of CAMEL. This choice was made because there is little

quantitative knowledge in the scientific literature expressing that rock fall susceptibility increases

beyond about 45 degrees. Conversely, this is what limit-equilibrium models describes. So 

another, perhaps more insightful, interpretation of the use of different DoS weights is that there

is more confidence in the knowledge expressed by the hGroundClass rules than for the
45

hSlopeAngle rules.

A design limitation encountered for the other landslide types (i.e., not illustrated in Table

17) is that for several static susceptibility rule-blocks there are more hSlopeAngle fuzzy values

than there are output fuzzy values (always five). This is the result of the different landslide

types being possible for a different range of pSlopeAngle values and the design criteria that there

be at least five values being available within the valid range for each landslide type. This is dealt

with in two different ways within the static susceptibility rule blocks. Either the output values

are increased by one fuzzy value for every two increasing fuzzy values of hSlopeAngle or

increasing DoS weights are used to differentiate the "very_high" output fuzzy value.

4.2.3.2 Modifiers Rule Block

The rules for the modifiers sub-module are shown in Table 18. (The rules for

RFallStaticFz are identical.) The concept behind the modifiers is that if some modifier condition 

exists, then the static susceptibility determined in the previous rule block should be increased or

decreased by one fuzzy value, depending. Further, input of data for modifier variables is

optional. There are 20 rules within the rule block. Five rules do not consider any of the three

modifiers and simply pass on the fuzzy value determined in the previous rule block. These rules

give greater weight to the knowledge from the previous rule-block. The remaining 15 rules

consider every combination of the three modifiers (one fuzzy value each) with the five fuzzy

values of RFallStaticInt––an intermediate variable, having the same specification as RFallStatic.

Each modifier rule is given a DoS weight of 1/n, where n is the number of modifiers considered for

the particular output fuzzy value. Using BSUM aggregation, the effect of the three modifiers is

cumulative. Without this design, having more than one modifier with a truth value of one (1)

would have the same effect as a single modifier having truth value of one (1). To ensure outputs

are monotonic, each fuzzy value of each modifier must be considered, even if the modifier does

not affect the output from the previous rule block––for example, the first rule in Table 18.
46

Table 18. Modifiers rule block for disrupted rock slides and falls.

IF THEN

hDisturbanceDist hMoisture RFallStaticInt hVegetation DoS RFallStatic

very_low dense 1.00 very_low

low dense 1.00 very_low

very_low 1.00 very_low

medium dense 0.34 low

not_very_low very_low 0.34 low

low 1.00 low

close very_low 0.34 low

high dense 0.34 medium

not_very_low low 0.34 medium

medium 1.00 medium

close low 0.34 medium

very_high dense 0.34 high

not_very_low medium 0.34 high

high 1.00 high

close medium 0.34 high

not_very_low very_high 0.34 very_high

not_very_low high 0.34 very_high

very_high 1.00 very_high

close high 0.34 very_high

close very_high 0.34 very_high

A limitation of the design of the modifier rule block using the Combs and Andrews (1998)

method is that for a given fuzzy value (assuming data is provided) two rules will be considered:

(1) the rule that considers the particular modifier (e.g., hVegetation) and the variable used to pass

the outputs from the susceptibility rule block (e.g., RFallStaticInt) and (2) the rule that simply

passes on the output from the susceptibility rule block. If the latter rule(s) were not specified,

no rules would result in a non-zero truth value in cases when no data is provided for any of the

modifiers. As designed, the result however is that a modifier does not have the effect of

increasing the susceptibility by one full fuzzy value, but rather some partial amount.

4.2.3.3 Seismic Hazard Rule Block

Figure 22 presents all of the rules that comprise the seismic hazard rule block for

disrupted rock slides and falls. The rules are the same for both RFallSeismic and RFallSeismicFz.

Also, because the historical abundance is nearly the same for rock and soil (Keefer, 1984), the
47

rules are the same for SFallSeismic and SFallSeismicFz. The rule block for determining the final

landslide concentration (and associated uncertainty) is different than the previous two rule blocks

in that it considers the intersection of all combinations of fuzzy values between RFallStatic and 

hShakeIntensity. The Combs and Andrews (1998) method is not to be appropriate for this rule

block because more control is required to enforce limiting conditions. That is, for example, if

RFallStatic is “very_low”, the final hazard must be “very_low.” With the Combs and Andrews

(1998) method, if the hShakeIntensity happened to be “extreme”, it would influence the output

excessively and result in a final concentration value significantly greater than the membership

function location for “very_low” of RFallSeismic.

Figure 22. Seismic variables rule block for disrupted rock slides and falls. VL = very_low,

L = low, M = medium, H = high, VH = very_high.

All rules within the rule block are given equal weight (DoS = 1.0). The two inputs are

combined using the PROD operator. BSUM is used for output aggregation to favor the output

fuzzy values with the most rules. Thus, BSUM favors “very_low” (and so on, favoring

“very_high” the least). If MAX output aggregation was used, the one rule mapping to 
48

“very_high” would have equal weight to all of the rules mapping to “very_low.”. All of the

quantitative landslide concentrations associated with “very_high” (corresponding to 100 ls/km2

for disrupted slides and falls) should be a rare occurrence because of the historical infrequency of

such concentrations.

The knowledge available within the scientific literature regarding the intersection of static

susceptibility and shaking intensity is not detailed enough for a one-to-one translation into fuzzy

rules. In light of this, initial rules were specified based on constraints described in the literature, in

addition to several pre-defined rule-specification criteria. The most general, but highest priority,

specification criterion is that the fuzzy surface represented by the shades of gray in Figure 34 

should be smooth and monotonic. That is, the output fuzzy values of adjacent rules should 

never be more than one fuzzy value apart (e.g., “low” to “medium”). The highest output fuzzy

value (“very_high” landslide concentration) should only be assigned to the “very_high” static

susceptibility. A “very_high” landslide concentration, in fact, represents an extreme case that is

historically very rare. A “very_low” landslide concentration should always be assigned to 

“very_low” susceptibility. A landslide concentration of “very_low” should be assigned to 

hShakeIntensity fuzzy values equal to or less than the corresponding indicator threshold for the

particular landslide type expressed in pShakeIntensity (Figure 8). Based on Keefer (2000) and 

Parise and Jibson (2000), landslide concentration should attenuate more rapidly with decreasing

static susceptibility than decreasing shaking intensity. In further support of this constraint,

Keefer (1984, Table 3) observes that large numbers of landslides (1,000 to 10,000) are possible

for moderate sized earthquakes (M 6). Lastly, the abundance table of Keefer (1984) (Table 28)

was used in relating the seismic hazard rule blocks between the landslide types in CAMEL that

have the same domain (or maximum possible concentration value). This was done by comparing

the total number of each output fuzzy value as a proxy for abundance and ensuring the relative

order of abundance is consistent with the observations of Keefer (1984). For example, the two 

rule blocks for disrupted soil slides, falls, and avalanches and for disrupted rock slides and rock 

falls are identical because these landslide types are approximately equally abundant. Conversely,

the rule blocks for soil slumps and rock slumps are different, with more rules resulting in 

“very_high” concentration for soil slumps. This reflects the higher historical abundance of soil
49

slumps and blocks slides over rock slumps and block slides.

5 CAMEL Implementation and Use

The previous section fully specified the fuzzy system that forms CAMEL. This section 

describes first how the fuzzy logic system design was implemented for computation of

earthquake-induced landslide hazard. The components of the implemented fuzzy system, which

can be integrated with Microsoft ExcelTM or other COM (common object model)-compliant

software programs, are presented. Second, the integration of the fuzzy logic system with ESRI

ArcGISTM for performing raster-based analysis is described. The setup, use and functionality of

the ArcObjectsTM tools are explained, followed by the logic and structure of the ArcObjectsTM

source code for CAMEL.

5.1 Implementation of CAMEL

The conceptual design of CAMEL, described in the above section, provides a blueprint

that anyone can use to implement CAMEL using the programming language or fuzzy systems

development tools of their choice. CAMEL is currently implemented using the fuzzy systems

development software FuzzyTechTM. With CAMEL implemented inside FuzzyTech, it is

necessary to export the fuzzy system to facilitate use of the model outside of the development

environment. The fuzzy system is exported as a single binary file (“CAMELddmmyy.ftr”) that

is used by the FuzzyTech Runtime DLL (“Ftrun32.dll”). (DLL stands for Dynamically Linked 

Libraries.) These and associated files are described in Table 19. The Runtime DLL can be

integrated with Microsoft ExcelTM, MATLABTM or other Microsoft WindowsTM COMcompliant software. A specific example of integrating CAMEL with COM-compliant software is

described below. (Integration with Microsoft ExcelTM is considerably more straightforward than 

the below example, simply requiring a cell-function reference to “CAMELddmmyy.ftr” to assign

each input variable and get each desired output.)
50

Table 19. Description of FuzzyTech files for integrating CAMEL with COM-compliant

software.

FuzzyTech Runtime DLL Files

Ftrun32.dll

The actual DLL file. Must be copied into “.../Windows/System32” folder and 

registered using regsvr32.exe.

Ftrun32.ini

Must be copied into “.../Windows” folder. A text file for modifying settings of

the Runtime DLL. Instructions for use in “Ftrun32.hlp”

Ftrun32.hlp

Help file; typically installed in the same folder as “Ftrun32.dll”. Double-click to 

open and read with Microsoft Help system.

Ftrun.bas

Visual Basic module file for use of “Ftrun32.dll” within Visual Basic or Visual

Basic for ApplicationsTM (VBA) code. Wrappers for Visual C++TM and JavaTM

can also be created.

CAMEL Files For FuzzyTech Runtime DLL

CAMELddmmyy.ftr (e.g., CAMEL082805.ftr)

CAMEL fuzzy system (binary file) for use with “Ftrun32.dll.” Any

modifications to the fuzzy system require exporting a new .ftr file.

CAMELddmmyy.io

Text file describing input and output variables for accessing

“CAMELddmmyy.ftr.”

CAMELddmmyy.cls

Visual Basic class file for use of “Ftrun32.dll” within Visual Basic or Visual

Basic for ApplicationsTM (VBA) code. Wrappers for Visual C++TM and JavaTM

can also be created.

5.2 Integration of CAMEL with ArcGISTM

CAMEL was designed specifically to be a regional-scale model for predicting

concentrations of all types of earthquake-induced landslides. Thus, at some level CAMEL must

be integrated with GIS or other spatial technologies. Software integration and custom
51

development in ArcGISTM is performed using ESRI’s ArcObjectsTM. ArcObjectsTM consists of

an object-oriented geographic data model and exposed software components, representing most,

if not all, of ArcGISTM functionality. ArcObjectsTM is implemented using Microsoft’s COM

protocol. The advantage of this implementation is that any COM-compliant software

development environment (e.g., Visual C++TM or Visual BasicTM) can be used to customize

ArcGISTM or add new functionality. Integration of CAMEL with ArcGISTM was done using

Visual BasicTM.

At this stage of development, the main objective was to integrate most of CAMEL’s

functionality with ArcGISTM so that spatial data layers (input data) can be used to compute

output layers for each landslide type. Developing a robust and user-friendly extension to 

ArcGIS was not an objective. Even so, CAMEL currently is relatively straightforward to setup

and use within ArcGIS. After setup, CAMEL is accessed through an ArcGIS map document

(“camelxx.mxd” with xx referring the compatible version of ArcGISTM, currently “camel90.mxd”).

The map document contains several pre-loaded map layers for referencing input raster data, as

well as a custom toolbar for running CAMEL.

5.2.1 ArcGISTM Setup and Use

Setup of CAMEL for use with ArcGISTM is simple. Copy the zip (archive) file

“camelxx.zip” into a new folder, where “xx” is a two-digit number referring to the version of

ArcGISTM the zipped files are compatible with (i.e., camel83.zip is compatible with version 8.3,

while camel90.zip is compatible with version 9.0). Double-click the zip file to open the archive

and access its contents. The file “Ftrun32.dll” must be copied into the “C:\Windows\System32”

folder on the computer, while “Ftrun32.ini” file must be copied into the “C:\Windows” folder.

The “Ftrun32.dll” file must be registered by typing “regsvr32 ftrun32.dll” at the Windows

command prompt. CAMEL then can be accessed by opening the map document (camelxx.mxd)

in ArcMapTM. Within the map document there is a custom toolbar (Figure 23) with three labeled

toolbar buttons for running CAMEL: “Calc All Hazard”, “Calc Rock Hazard”, and “Calc Rock 

Hazard.” The functions associated with each button are used in the same way and requires, for

the most part, the same input layers. The difference is in the required layer for describing
52

rock/soil properties, as well as the subsequent output layers (described below).

Figure 23. CAMEL implementation within ArcGISTM. Map layer names shown are

required for CAMEL to find the respective input data.

Prior to running any of the functions, the input layers must be associated with the desired 

spatial data source for each input variable. The function looks in the ArcMap table of contents

for specifically labeled raster map layers corresponding to each of CAMEL’s input variables.

The input layer labels adhere to a strict naming convention and capitalization in order for the

function to find the layer and use it appropriately. The labeled input layers, reflecting the

required label convention and capitalization, are listed in Figure 36. Any other layers added to 

the table of contents are ignored.

To associate the desired data with each respective input layer, right click on each layer in 

the table of contents and select “Data Source” to bring up the data source dialog (Figure 24).

Click on the “Set Data Source” button in the dialog to find and assign the appropriate raster data

set. This process must be done with each input layer, with the exception of “GroundClass”,

“RockClass” and “SoilClass.” If the “Calc All Haz” function is to be used, the “GroundClass”

layer must be specified. The “GroundClass” layer will then serve as an overall index of material

properties for rock and soil; that is, the same properties will be used in assessing both rock and
53

soil hazard. If “Calc Rock Hazard” is to be run, “RockClass” must be specified. If “Calc Soil

Hazard” is to be run, “SoilClass” must be specified. While data can be missing for CAMEL (the

fuzzy system) to run, currently the ArcGIS functions require that all layers be specified. So if

no data is available, simply associate the particular layer with a raster of NO DATA. A 3x3-cell

ArcGIS grid corresponding to each CAMEL input variable is provided in the camelxx.zip archive

file in order to do a test setup and run of the model.

Figure 24. Data source dialog in ArcMapTM for assigning input raster data to appropriate

input map layers.

Once the input data has been associated with the input layers, the respective CAMEL

functions can be executed by clicking on the corresponding button in the CAMEL toolbar. The

user is then presented with two input dialog boxes before hazard calculation begins (Figure 25).

The first dialog box asks for a suffix text string that will be appended to each output raster file

created. This is a convenience so that the user can run and keep track of outputs from several

scenario (e.g., h_rockav1, h_rockav2; or h_rockav_wet, h_rockav_dry, etc.). The second dialog

box asks for a minimum truth-value to be specified. The minimum truth-value reflects the lowest

possibility value (from 0 to 1) that can be calculated for a particular landslide type and not be

considered impossible (i.e., no hazard).
54

Figure 25. Input dialog boxes for running CAMEL functions.

After the second dialog box is completed, the respective function enumerates through all

of the cells in the raster layer, passing the data values from each input cell to CAMEL (via the

FuzzyTechTM Runtime DLL). CAMEL then returns an array of landslide concentration values

corresponding to either all six landslide types or just the three rock landslide types or just the

three soil landslide types, depending on the particular function. New raster layers are created to 

store the respective array of output values from CAMEL. These layers are automatically added

to the ArcMap table of contents and appropriately labeled (Figure 26). Currently, in order to 

run another scenario, it is necessary to change the names of the previously created output layers

(e.g., from “Soil Slump Hazard” to “Soil Slump Hazard Dry”).
55

Figure 26. Example of output layers created after running "Calc All Hazard."

6 References

Berkan, R. and Trubatch, S. (1997) Fuzzy Systems Design Principles: Building Fuzzy IF-THEN

Rule Bases. New York: IEEE Press.

Bommer, J. and Rodriguez, C. (2002) Earthquake-induced landslides in Central America.

Engineering Geology, 63, 189– 220.

Combs, W.E. and Andrews, J.E. (1998) Combinatorial rule explosion eliminated by a fuzzy rule

configuration. IEEE Transactions on Fuzzy Systems, 6, 1-11.

Cruden, D. and Varnes, D. (1996) Landslide types and processes. in A. Turner and R. Schuster

(Ed.), Landslides: Investigations and Mitigation (pp. 36-75). Washington, D.C.:

Transportation Research Board.

Guillaume, S. (2001) Designing fuzzy inference systems from data: An interpretability-oriented

review. IEEE Transactions on Fuzzy Systems, 9, pp 426-443.

Hancox, G., Perrin, N. and Dellow, G. (2002) Recent studies of historical earthquake-induced 

landsliding, ground damange, and MM intensity in New Zealand. Bulletin of the New

Zealand Society for Earthquake Engineering, 35, 59-95.
56

Hansen, A. and Franks, C. (1991) Characterisation and mapping of earthquake triggered 

landslides for seismic zonation. Proceedings of the Fourth International Conference on 

Seismic Zonation (pp. 149-195). Palo Alto, CA.

Jibson, R., Harp, E., and Michael, J. (1998) A method for producing digital probabilistic seismic

landslide hazard maps. U.S. Geological Survey Open-File Report 98-113, 17, 2 pl.

Keefer, D.K. (1984) Landslides caused by earthquakes. Bulletin of the Geological Society of

America, 95, 406-421.

Keefer, D.K. (1993) The susceptibility of rock slopes to earthquake-induced failure. Association 

of Engineering Geologists Bulletin, 30, 353--361.

Keefer, D.K. (Ed.) (1998) The Loma Prieta, California, Earthquake of October 17, 1989 --

Landslides. U.S. Geological Survey Professional Paper 1551-C.

Keefer, D.K. (2000) Statistical analysis of an earthquake-induced landslide distribution - The

1989 Loma Prieta, California event. Engineering Geology, 58, 231-249.

Lazzerini, B. and Marcelloni, F.. (2000) Reducing computation overhead in MISO fuzzy 

systems. Fuzzy Sets and Systems, 113, 485-496.

Liao, C., Lee C.T., Liao, and Liao, H. (2002) Statistical analysis of factors affecting landslides

triggered by the 1999 Chi-Chi, Taiwan Earthquake. American Geophysical Union

Meeting. San Francisco.

Parise, M. and Jibson, R. (2000) A seismic landslide susceptibility rating of geologic units based 

on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge,

California earthquake. Engineering Geology, 58, 251–270.

Rodriguez, C., Bommer, J. and Chandler, R. (1999) Earthquake-induced landslides: 1980-1997.

Soil Dynamics and Earthquake Engineering, 18, 325–346.

Wald, D.Q.V., Heaton, T., Kanamori, H., Scrivner, C. and Worden, C. (1999) TriNet

“ShakeMaps”: Rapid generation of instrumental ground motion and intensity maps for

earthquakes in Southern California. Earthquake Spectra, 15, 537-556.

Zadeh, L. (1965) Fuzzy sets. Information and Control, 8, 338-353.
57

Appendix A: Complete Specification of Hazard Module RuleBlocks for CAMEL

This appendix presents all of the rule-blocks and corresponding rules that comprise the

hazard module of CAMEL. The three rule-blocks – static susceptibility, modifiers, and seismic

hazard – are presented in turn for each of the six landslide types considered by CAMEL. All

rule-blocks use PROD operators if there is more than one input and BSUM aggregation.

A.1 Rock Avalanche

Table A1. Static susceptibility rule block for rock avalanches.

IF THEN

hGroundClass LandslideType hSlopeAngle DoS RAvStaticInt

very_good rock_avalanche 0.75 very_low

good rock_avalanche 0.75 low

moderate rock_avalanche 0.75 medium

poor rock_avalanche 0.75 high

very_poor rock_avalanche 0.75 very_high

rock_avalanche medium 0.25 very_low

rock_avalanche medium_steep 0.25 low

rock_avalanche steep 0.25 medium

rock_avalanche very_steep 0.25 high

rock_avalanche extremely_steep 0.25 very_high

rock_avalanche nearl_vertical 0.40 very_high
58

Table A2. Modifiers rule block for rock avalanches.

IF THEN

hDisturbanceDist RAvStaticInt DoS RAvStatic

very_low 1.00 very_low

low 1.00 low

medium 1.00 medium

high 1.00 high

very_high 1.00 very_high

close very_low 0.50 low

close low 0.50 medium

close medium 0.50 high

close high 0.50 very_high

close very_high 0.50 very_high

Figure A1. Seismic hazard rule block for rock avalanches. VL = very_low, L = low, M =

medium, H = high, VH = very_high.
59

A.2 Rock Fall and Disrupted Rock Slides

Table A3. Static susceptibility rule block for disrupted rock falls and slides.

IF THEN

hGroundClass LandslideType hSlopeAngle DoS RFallStaticInt

very_good rock_fall 0.80 very_low

good rock_fall 0.80 low

moderate rock_fall 0.80 medium

poor rock_fall 0.80 high

very_poor rock_fall 0.80 very_high

rock_fall medium_steep 0.20 very_low

rock_fall steep 0.20 low

rock_fall very_steep 0.20 medium

rock_fall extremely_steep 0.20 high

rock_fall nearl_vertical 0.20 very_high

Table A4. Modifiers rule block for disrupted rock falls and slides.

IF THEN

hDisturbance

Dist

hMoisture RFallStaticInt hVegetation DoS RFallStatic

very_low dense 1.00 very_low

low dense 1.00 very_low

very_low 1.00 very_low

medium dense 0.34 low

¬very_low very_low 0.34 low

low 1.00 low

close very_low 0.34 low

high dense 0.34 medium

¬very_low low 0.34 medium

medium 1.00 medium

close low 0.34 medium

very_high dense 0.34 high

¬very_low medium 0.34 high

high 1.00 high

close medium 0.34 high

¬very_low very_high 0.34 very_high

¬very_low high 0.34 very_high

very_high 1.00 very_high

close high 0.34 very_high

close very_high 0.34 very_high
60
61

Figure A2. Seismic hazard rule block for disrupted rock slides and falls. VL = very_low, L

= low, M = medium, H = high, VH = very_high.

A.3 Rock Slumps and Rock Block Slides

Table A5. Static susceptibility rule block for rock slumps and block slides.

IF THEN

hGround

Class

Landslide

Type

hMoisture hSlopeAngle DoS RSlump

StaticInt

very_good rock_slump 0.40 very_low

good rock_slump 0.40 low

moderate rock_slump 0.40 medium

poor rock_slump 0.40 high

very_poor rock_slump 0.40 very_high

rock_slump very_low 0.40 very_low

rock_slump low 0.40 low

rock_slump medium 0.40 medium

rock_slump high 0.40 high

rock_slump very_high 0.40 very_high

rock_slump gradual 0.20 very_low

rock_slump medium_gradual 0.20 low

rock_slump medium 0.20 medium

rock_slump medium_steep 0.20 high

rock_slump steep 0.20 very_high

rock_slump very_steep 0.30 very_high

rock_slump extremely_steep 0.40 very_high

rock_slump nearl_vertical 0.50 very_high
62

Table A6. Modifiers rule block for rock slumps and block slides.

IF THEN

hDisturbanceDist RSlumpStaticInt hVegetation DoS RSlumpStatic

very_low 1.00 very_low

very_low dense 1.00 very_low

low dense 1.00 very_low

low 1.00 low

medium dense 0.50 low

close very_low 0.50 low

medium 1.00 medium

high dense 0.50 medium

close low 0.50 medium

high 1.00 high

very_high dense 0.50 high

close medium 0.50 high

very_high 1.00 very_high

close high 1.00 very_high

close very_high 1.00 very_high

Figure A3. Seismic hazard rule block for rock slumps and block slides. VL = very_low, L =

low, M = medium, H = high, VH = very_high.
63

A.4 Disrupted Soil Slides, Soil Falls, and Soil Avalanches

Table A7. Static susceptibility rule block for soil falls, slides, and avalanches.

IF THEN

hGroundClass LandslideType hSlopeAngle DoS SFallStaticInt

very_good soil_fall 0.75 very_low

good soil_fall 0.75 low

moderate soil_fall 0.75 medium

poor soil_fall 0.75 high

very_poor soil_fall 0.75 very_high

soil_fall gradual 0.25 very_low

soil_fall medium_gradual 0.25 low

soil_fall medium 0.25 low

soil_fall medium_steep 0.25 medium

soil_fall steep 0.25 medium

soil_fall very_steep 0.25 high

soil_fall extremely_steep 0.25 high

soil_fall nearl_vertical 0.25 very_high
64

Table A8. Modifiers rule block for disrupted soil falls, slides and avalanches.

IF THEN

DisturbanceDist Moisture SFallStaticI

nt

Vegetation DoS SFallStatic

very_low dense 1.00 very_low

low dense 1.00 very_low

very_low 1.00 very_low

medium dense 0.34 low

close very_low 0.34 low

¬very_low very_low 0.34 low

low 1.00 low

high dense 0.34 medium

close low 0.34 medium

¬very_low low 0.34 medium

medium 1.00 medium

very_high dense 0.34 high

close medium 0.34 high

¬very_low medium 0.34 high

high 1.00 high

close high 0.50 very_high

close very_high 0.50 very_high

¬very_low high 0.50 very_high

¬very_low very_high 0.50 very_high

very_high 1.00 very_high
65

Figure A4. Seismic hazard rule block for disrupted soil slides and falls. VL = very_low, L

= low, M = medium, H = high, VH = very_high.
66

A.5 Soil Slumps and Soil Block Slides

Table A9. Static susceptibility rule block for soil slumps and block slides.

IF THEN

Ground

Class

Landslide

Type

Moisture SlopeAngle DoS SSlump

StaticInt

soil_slump very_gradual 0.20 very_low

soil_slump gradual 0.20 very_low

soil_slump medium_gradual 0.20 low

soil_slump medium 0.20 medium

soil_slump medium_steep 0.20 high

soil_slump steep 0.20 very_high

soil_slump very_steep 0.30 very_high

soil_slump extremely_steep 0.40 very_high

soil_slump nearl_vertical 0.50 very_high

very_good soil_slump 0.40 very_low

good soil_slump 0.40 low

moderate soil_slump 0.40 medium

poor soil_slump 0.40 high

very_poor soil_slump 0.40 very_high

soil_slump very_low 0.40 very_low

soil_slump low 0.40 low

soil_slump medium 0.40 medium

soil_slump high 0.40 high

soil_slump very_high 0.40 very_high

Table A10. Modifiers rule block for soil slumps and block slides.
67

IF THEN

DisturbanceDist SSlumpStaticInt Vegetation DoS SSlumpStatic

very_low dense 1.00 very_low

low dense 1.00 very_low

very_low 1.00 very_low

medium dense 0.50 low

low 1.00 low

close very_low 0.50 low

high dense 0.50 medium

medium 1.00 medium

close low 0.50 medium

very_high dense 0.50 high

high 1.00 high

close medium 0.50 high

very_high 1.00 very_high

close high 1.00 very_high

close very_high 1.00 very_high

Figure A5. Seismic hazard rule block for soil slumps and block slides. VL = very_low, L =

low, M = medium, H = high, VH = very_high.

A.6 Rapid Soil Flows

Table A11. Static susceptibility rule block for rapid soil flows.
68

IF THEN

GroundClass LandslideType SlopeAngle DoS SFlowStaticInt

very_good soil_flow 0.75 very_low

good soil_flow 0.75 low

moderate soil_flow 0.75 medium

poor soil_flow 0.75 high

very_poor soil_flow 0.75 very_high

soil_flow very_gradual 0.25 very_low

soil_flow gradual 0.25 very_low

soil_flow medium_gradual 0.25 low

soil_flow medium 0.25 low

soil_flow medium_steep 0.25 medium

soil_flow steep 0.25 medium

soil_flow very_steep 0.25 high

soil_flow extremely_steep 0.25 high

soil_flow nearl_vertical 0.25 very_high

Table A12. Modifiers rule block for rapid soil flows.

IF THEN

Disturbance

Dist

Moisture SFlow

StaticInt

Vegetation DoS SFlow

Static

very_low dense 1.00 very_low

low dense 1.00 very_low

very_low 1.00 very_low

very_high very_low 0.34 low

medium dense 0.34 low

low 1.00 low

close very_low 0.34 low

very_high low 0.34 medium

high dense 0.34 medium

medium 1.00 medium

close low 0.34 medium

very_high medium 0.34 high

very_high dense 0.34 high

high 1.00 high

close medium 0.34 high

very_high high 0.50 very_high

very_high very_high 0.50 very_high

very_high 1.00 very_high

close high 0.50 very_high

close very_high 0.50 very_high
69

Figure A6. Seismic hazard rule block for rapid soil flows. VL = very_low, L = low, M =

medium, H = high, VH = very_high.