10th Asian Regional Conference of IAEG (2015)

Review and Perspectives on Methodology for Landslide Hazard Analysis
Chyi-Tyi LEE(1)(1) Graduate Institute of Applied Geology, National Central University, Taiwan
E-mail:ct@ncu.edu.tw
Abstract
Landslide hazard analysis aims at quantitatively evaluating the probability oflandslide failure within a specified period of time and within a given region. It is different
from a landslide susceptibility analysis which only classifies a region into several
successive classes with different potential of landsliding. This study reviews mainly forrain-induced landslide hazard. Similar methodology may also be applied to earthquakeinduced landslides.
There are three different approaches in modern landslide hazard analysis. (1)Deterministic approach by an infinite slope model and a hydrological model withparameters pre-calibrated by a set of landslide data is applied, and a return-period rainfall
may be used as final input for a hazard map. (2) Probabilistic approach by the Poissonmodel with annual landslide rate at a slope unit derived from a multi-temporal landslide
inventory is applied, and an annual exceedence probability of certain magnitude oflandslide failure at a specific slope is provided. (3) Statistical approach by a multi-stage ofanalysis, including an event-based landslide susceptibility analysis, a probability of failure
analysis, a rainfall frequency analysis, and a final analysis, is used for hazard mapping.
All the three approaches may be used for regional landslide hazard mapping, but,there are some shortages in each kind of approach. The deterministic approach requires soil
strength, soil depth, and hydrological parameters, which are difficult to collect in a large
region. The probabilistic approach requires a long-period landslide data, which may beinsufficient length of historical records, incompleteness in inventory, and a possible mixingof extreme events and/or earthquake disturbance. The statistical approach looks better ascomparing with the weakness of the other two approaches, but, there still are some further
developments needed to predict landslide magnitude and run-out distance which are very
important in risk assessment.
Keywords: Landslide inventory, Landslide susceptibility, Landslide hazard, Hazard mapping
1.

Introduction(LHA) is a regional hillslope stability analysis
concerning the probability of instability at any given
point within a specified period of time. It aims at
quantitative evaluation of spatial-temporal probabilityof landslide at a given slope. Landslide susceptibility
analysis (LSA) only classifies a region into several
successive classes with different potential oflandsliding, and is different from LHA.
There are mainly three different approaches inmodern LHA. (1) Deterministic approach by an
infinite slope model and a hydrological model(Montgomery and Dietrich, 1994; Iverson, 2000;
Claessens et al., 2007). (2) Probabilistic approach by
the Poisson model with annual landslide rate at aslope unit derived from a multi-temporal landslide
inventory (Guzzetti et al., 2005). (3) Statistical
approach by a multi-stage of analysis, including an

There always had some problems on landslide
susceptibility/hazard evaluation in the past. These
included: (1) the problem of attempting to quantify
landslide hazard over larger areas, (2) challenge of
mixing landslide types in the analysis, (3) to use a
multi-temporal landslide inventory or an event
landslide inventory, (4) the selection of mapping unit
in the analysis, and also, (5) the selection of theanalytical method (Guzzetti et al., 1999; Aleotti et al.,
1999; Brenning, 2005; Van Westen et al., 2006; Lee
and Fei, 2015).
Varnes et al. (1984) defined the landslide hazard
as “the probability of occurrence within a specified
period of time and within a given area of a potentiallydamaging phenomenon”. Landslide hazard analysis

1

10th Asian Regional Conference of IAEG (2015)

event-based susceptibility analysis, a probability of
failure analysis, a rainfall frequency analysis, and a
final analysis, is used for hazard mapping (Lee andFei, 2015, Lee et al., 2008). A comparison of thesethree approaches and an overview of recent
developments in the landslide hazard analysis andmapping at medium scales will be given. Which kindof approach is more appropriate and more promisingin the further development of LHA will also be
discussed.
It is clear that temporal probability is an importantcomponent in the LHA. Some previous studies,
although include the wording like hazard or risk, areactually involving susceptibility only, and are notincluded in the present review.
2.

development and upgrade to LHA, because certain
return-period rainfalls can be input into thesusceptibility model, and makes the model temporal
meaningful.
In the event-based LSA, using the susceptibility
model and the training data, a probability of failureand susceptibility relationship can be built, and theprobability of failure curve can be used to map
landslide spatial probability of the study region.
Therefore, spatial probability and temporal probability
are both taken into account. The outcome is alandslide probability of failure map under a certain
return-period rainfalls and this would be a kind oflandslide hazard map (Lee and Fei, 2015).
This kind of landslide hazard model is good formapping landslide hazard, prediction of landslide
failure probability under a rain event, decision
making for regional planning, site selection, hazard
mitigation, and estimation of sediment yield for adrainage basin after a rain event. However, there still
are some further developments needed to predictlandslide magnitude and run-out distance which arevery important in risk assessment.

Comparison of Different Approaches

2.1 Deterministic approach
Deterministic approach performs hazard analysis
by an infinite slope model and a hydrological modelwith parameters pre-calibrated by a set of landslidedata, and a return-period rainfall may be used as a
final input for the hazard map (Montgomery andDietrich, 1994; Iverson, 2000; Claessens et al., 2007).
This physical-based method is theoretically perfect,
but it is practically doubtful. Because it requires soil
strength, soil depth, and hydrological parameters, andthese are difficult to collect in a large region.

2.4 Comparison and discussions
Not resemble previous LSAs, all the three
approaches are capable of hazard mapping over large
areas. This is because LHAs present consistenthazard-level under certain return-period or annual
exceedence probability over a large region.
As to the types of landslide involved, thedeterministic approach and the statistical approach
are designed for the type of shallow landslide only,
whereas the probabilistic approach is not restricted toshallow landslides; deep landslides are not excludedin the analysis. Flow type of landslide, lateral spread,and slope deformations are excluded in all of thethree approaches. Separation of different types oflandslide, and to build a hazard model for a type oflandslide are deemed necessary in the landslide
hazard study of a region.
A multi-temporal landslide inventory is requiredin the probabilistic approach, whereas an event
landslide inventory is used both in the deterministic
approach and the statistical approach. The latter ismuch easier to prepare than the formal, not tomention that to prepare a long-period and complete
landslide inventory is almost impossible. This isespecially true for earthquake-induced landslides.
In the deterministic approach and the statistical
approach, grid-cells are used for the analysis, whereas
slope-units are used in the probabilistic approach. The
formal has a higher resolution (about 100m2 a unit),
and the latter has a lower resolution (about 100000m2
a unit). But, hazard-level in the formal is discrete, thatin the latter is compacted and is easy to be used inhazard management and land use. This may meansthat vectorization and compaction of the discrete

2.2 Probabilistic approach
Like conventional seismic hazard analysis forearthquake magnitude, the probabilistic approach forlandslide hazard proposed by Guzzetti et al. (2005) isvery similar with. It used annual landslide rate at aslope unit derived from a multi-temporal landslide
inventory and applied the Poisson model to find anannual exceedence probability of certain magnitudeof landslide failure at each slope unit over a mediansized drainage basin.
The including of landslide magnitude in LHA is
first proposed in this study. Their methodology issimple and straightforward. However, the time spanof the landslide inventory may be not long enough tocover an active period and a calm period so that themeaning of the result is reduced. And also, thecompleteness of the inventory may be difficult toachieve. Mixing of extreme events and/or earthquake
disturbance is also possible and makes the inventory
complicated for use.
2.3 Statistical approach
Conventional multivariate statistical analysis via
discriminate analysis or a logistic regression forlandslide susceptibility is a kind of statistical
approach. Among the statistical methods, theevent-based LSA introduced by Lee et al. (2008) is a
recent progress which may be possible for further

2

10th Asian Regional Conference of IAEG (2015)

grid-cells in the deterministic approach and thestatistical approach would be necessary. This isstrongly recommended to be considered in the futurestudies.
Which kind of approach is more appropriate andmore promising in the further development? First, theimprovement of data quality in the deterministic
approach may be a control of further development of
this method. In-depth review and ideas should benecessary, and the consideration of multivariate
geostatistical interpolation may be one of the possible
solution. Second, to prepare a long-period andcomplete landslide inventory is not easy in mostcountries. Unfortunately, this method is also highly
controlled by the data itself, and is limited for further
development. The statistical approach requires only
an event landslide inventory accompanying thetrigger factors and other data set to build the hazard
model. This is deemed more appropriate inconventional usage in every county. However, thestatistical approach requires enough number of rain
gauge stations, so that a better interpolation of rainfall
distribution can be drawn in the study region. The
shortage of the statistical approach is also thatlandslide magnitude and run out distance cannot be
predicted at the present stage and reserve further
development.
Landslides are secondary or induced features,
whose recurrence is controlled by the repetition of
triggering events, such as earthquakes or heavy
rainfalls. Therefore, LHA may be separated into atleast two stages, that is a spatial probability stage anda temporal probability stage. This is true both in thedeterministic approach and in the statistical approach.
The multi-stage procedure in the above-mentioned
statistical approach is deemed necessary.
3.

abFig. 1 Basic susceptibility map at mountain terrain of
the Choswei River catchment in central Taiwan, (a)that from event-independent model of Typhoon Toraji
event, (b) that from multi-temporal landslide inventory.
3.2 Probability of failure surface
We tested the relationship between probability of
failure and rainfall intensity, as well as total rainfall ateach event-independent susceptibility bin. It was
found that the relation is good; the probability of
failure increases with an increase in the rainfalls andalso an increase in the susceptibility. A fitting surface
of probability of landslide failure using a rainfall
parameter and the event-independent susceptibility as
two independent variables was done. The result isshown in Fig. 2 and equations 1 and 2 as follows:

Recent Progress in Statistical Approach

3.1 Basic susceptibility of a region
When event landslide inventory and triggeringfactors are used in building a susceptibility model, thesusceptibility model is dependent on the event itself.
However, if we extract the component of the triggeringfactors from the model, then the model becomes
event independent, provided that the triggering factor
is an independent factor having only small correlation
coefficient with each causative factor.
An event-independent susceptibility model istested to be a basic susceptibility model in Lee et al.
(2004), and in our recent studies. Different eventindependent susceptibility model for the same region
are similar in pattern, and is similar also to asusceptibility model built by a multi-temporallandslide inventory at that region (Fig. 1). On thisbasis, we can use an event-independent susceptibility
model to represent the susceptibility of the region
with confidence.

0.760)(1  e0.768)(1  ey  26.815 (1  e -3.758
y  32.357 (1  e -3.843

- 2.0175 (-1.6835 (x1 1.836)100

x 2 2.445
)
2000

) ,

[1]

) ,

[2]

where x1 is the maximum rainfall intensity inminimeter, x2 is the total rainfall in minimeter; y is theprobability of landslide failure, and λ is the basic
susceptibility. Equation 1 is for maximum rainfall
intensity and eq. 2 is for the total rainfall of an rain
event.

3

10th Asian Regional Conference of IAEG (2015)

30

25

Probability of Failure, %the study region under a scenario rain event or
return-period rainfalls. Figure 3 is an example oflandslide hazard map under 100-year rainfall.

LEGENDLSS 0.9~1.0
LSS 0.8~0.9
LSS 0.7~0.8
LSS 0.6~0.7
LSS 0.5~0.6
LSS 0.4~0.5
LSS 0.3~0.4
LSS 0.2~0.3
LSS 0.1~0.2
LSS 0.0~0.1

20

15

10

5

0
0

20

40

60

80

100

Rainfall Intensity, mm

(a )
35

LEGENDLSS 0.9~1.0
LSS 0.8~0.9
LSS 0.7~0.8

Probability of Failure, %30

LSS 0.6~0.7
LSS 0.5~0.6
LSS 0.4~0.5
LSS 0.3~0.4
LSS 0.2~0.3
LSS 0.1~0.2
LSS 0.0~0.1

25
20
15
10
5

Fig. 3 100-year rainfall landslide hazard map for the
Kaoping River basin in southern Taiwan.

0
0

500

1000

1500

2000

2500

Total Rainfall, mm(b )Fig. 2 An example of probability of failure surface
from the Kaoping River basin in southern Taiwan, (a)maximum rainfall intensity, (b) total rainfall.
Equations are shown in the text.

3.3 Nationwide large-scale landslide hazard map
In the past, landslide susceptibility analysis was
generally limited to a small area and of research
purpose. A susceptibility model established for on
specific region could not be applied to other areas.
This has led to technological stagnation in the
research and testing phase.
Based on actual demand, Taiwan started a
research program, the Central Geological Survey, in
2003, for the execution of nationwide landslide
hazard maps. By the end of year 2013, the whole of
Taiwan has been mapped with good results. The
statistical approach proposed by our group wasdeveloped in this period. For hazard analysis andmapping in a wide region, one needs to take into
account heterogeneous features present in the wide
area and the consistency of results from different
regions. These challenges require the following two
methods to be resolved: (1) division of homogeneouszones so that a reliable hazard model can beestablished for a zone; (2) a consistent hazard levelmust be chosen so that the results for different
regions will be consistent. The statistical approach via
an event-based landslide susceptibility model and aprobability of failure surface will ensure theconsistency of results from different analyses.

A global probability of failure surface for two
rainfall variables may be built by combining
equations 1 and 2. If the two rainfall variables aretotally independent, then the square of global
probability of failure is the sum of the square of eq. 1
and the square of eq. 2. If the two rainfall variables
are totally dependent, then the square of global
probability of failure is the product of the eq. 1 andthe eq. 2. In actual cases, the two rainfall variables
are commonly in between dependent and independent,
but have a correlation coefficient between 0 and 1. In
the present study, we propose,22y  ((1  r )( y1  y2 )  r ( y1 y2 )) 0.5 ,[3]where y1 is y of eq. 1, y2 is y of eq. 2, and r is thecorrelation coefficient between maximum rainfall
intensity and the total rainfall.
The probability of failure surface may be used tomap and/or predict landslide spatial probability over4

10th Asian Regional Conference of IAEG (2015)

A total of 16 river basins and 26 susceptibility
models, as well as probability of failure curves, havebeen built during the period 2007 to 2013. For
consistent mapping, a rainfall return-period must be
selected. We adopt the 100-year return-period rainfall
for the triggering factors and use them in the hazard
mapping. A 100-year shallow landslide probability
map for the whole of Taiwan (1:50,000 scale andtotal area 35,873 km2) is shown in Fig. 4. It shows aconsistent hazard level (landslide spatial probability)
in the region.

hazard study, including run out area, is also ongoing.
4. Concluding Remarks and Perspectives
Statistical approach by a multi-stage of analysis,
including an event-based landslide susceptibility
analysis, a probability of failure analysis, a rainfall
frequency analysis, and a final analysis, is deemed
best among the three approaches for landslide hazard
analysis (LHA) from the practical point of view.
Susceptibility indices are only relative values in astudy area where a model was built. Samesusceptibility index from two different models do notmean equal potential for landsliding. They even haveno relation with each other. However, a probability oflandslide failure (PLF) means a kind of hazard levelin LHA. Same PLF at different model means samething: percentage of failed grid-cells from the total
grid-cells in a susceptibility bin. When working overa large region, PLFs from different models are
consistent in meaning. Therefore, mapping a large
region with multi-models will be consistent in result.
No seam or no abrupt change will exist at theboundary of two models.
The term “basic susceptibility” was firstly introduced
by Lee et al. (2004), and is mentioned now in thisstudy. This term links up the result of a conventional
LSA and an event-based LSA, because their basic
susceptibility is similar. The basic susceptibility canbe used to build a susceptibility, rainfall and PLF
relationship, and forms an empirical PLF equation fora region. This could be used in mapping for a
scenario rain event or a return-period rainfall.
To optimize the construction of a basic
susceptibility model for a region, mixing two or morerain events to build an event-independent landslide
susceptibility model, or using multi-temporallandslide inventory to directly build a basic
susceptibility model may be considered. Using along-period multi-temporal landslide inventory tobuild a basic susceptibility model for a region is best
according to our experiences. Mixing two or morerain events in building a basic susceptibility model issecond. Using only one event is OK, if no more data
is available. By the way, using multi-event landslide
data set is good also for constructing a more generalprobability of failure surface.
For the statistical approach, there still are somefurther developments needed to predict landslide
magnitude and run-out distance which are very
important in risk assessment. This is actively studied
by a Ph.D. student in our Institute. The analysis oflandslide magnitude is basically slope-unit based.
For the deterministic approach, since the soil
depth, strength and hydrological data are thecontrolling factors; to improve the data quality bymultivariate geostatistical method may be necessary.
The basic susceptibility model may be used as an
auxiliary variable in the multivariate geostatistics forFig. 4 100-year rainfall landslide hazard map for thewhole of Taiwan (modified after Lee and Fei, 2015).
Original map scale is 1 to 50,000.
Using similar approach, we also complete anationwide debris-flow hazard map in the same
project. A preliminary deep-seated landslide
susceptibility evaluation has also been done, and the
deep-seated landslide hazard mapping is actively
ongoing. For rockfall hazard, it is partly included in
the shallow landslide hazard. But, a separate rockfall

5

10th Asian Regional Conference of IAEG (2015)better interpolation.
For the probabilistic approach, a long-period
landslide inventory is the control. If data covers longenough time span to cover an active period and acalm period, then the result would be meaningful.
The completeness of the multi-temporal landslide
inventory is another issue. If the inventory is
incomplete, the total duration method cannot beapplied. Instead of this, the extreme value method
may be considered.
For earthquake-induced landslides, a probabilistic
approach is not applicable, because of lacking a
multi-temporal landslide inventory. Deterministic
approach by using Newmark displacement method(Jibson et al., 2000), and statistical approach with theearthquake intensity as a triggering factor (Lee, 2014)
are working well. This deterministic approach hassimilar problem with the above-mentioned one, bothare lacking of soil depth and strength data. The
statistical approach is again more promising andperspective.

P. (1999): Landslide hazard evaluation: a review ofcurrent techniques and their application in a
multi-scale study, Central Italy, Geomorphology,
Vol. 31, pp. 181-216.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M.,
and Ardizzone, F. (2005): Probabilistic landslide
hazardassessment
at
the
basin
scale,
Geomorphology, Vol. 72, pp. 272-299.
Iverson, R.M. (2000): Landslide triggering by rain
infiltration, Water Resources Research, Vol. 36, pp.
1897-1910.
Jibson, R.W., Harp, E.L., and Michael, J.A. (2000): A
method for producing digital probabilistic seismic
landslide hazard maps, Engineering Geology, Vol.
58, pp. 271-289.
Lee, C.T. (2014): Statistical Seismic Landslide
Hazard Analysis: an Example from Taiwan,
Engineering Geology, Vol. 182, pp. 201-212.
Lee, C.T., and Fei, L.Y. (2015): Nationwide landslide
hazard analysis and mapping in Taiwan. In:
Engineering Geology for Society and Territory,
Springer International Publishing, Vol. 2, pp.
971-974.
Lee, C.T., Huang, C.C., Lee, J.F., Pan, K.L., Lin,
M.L., and Dong, J.J. (2008): Statistical approach tostorm event-induced landslide susceptibility,
Natural Hazard and Earth System Sciences, Vol. 8,
pp. 941-960.
Lee, C.T., Huang, C.C., Lee, J.F., Pan, K.L., Lin,
M.L., Liao, C.W., Lin, P.S., Lin, Y.S., and Chang,
C.W. (2004): Landslide susceptibility analyses
based on three different triggering events and resultcomparison, Proceeding of International Symposiumon Landslide and Debris Flow Hazard Assessment,
Taipei, pp. 6-1~6-18.
Montgomery, D.R., and Dietrich, W.E. (1994): A
physical-based model for the topographic control onshallow landsliding, Water Resources Research, Vol.
30, No. 4, pp. 1153-1171.
Van Westen, C.J., van Asch, T.W.J., and Soeters, R.
(2006): Landslide hazard and risk zonation - why is
it still so difficult? Bulletin of Engineering Geology
and Environment, Vol. 65, pp. 167-184.
Varnes, D.J., and IAEG Commission on Landslides
and other Mass-Movements, (1984): Landslide
Hazard Zonation: a Review of Principles andPractice, NESCO Press, Paris. 63p.

Acknowledgements
This research was partly supported by the Central
Geological Survey, Ministry of Economic Affairs,
Taiwan, under the Landslide Research Project. The
author is much appreciated to the long-term project
support so that everything can be carried out
smoothly.
References
Aleotti, P. and Chowdury, R. (1999): Landslide
hazard assessment: summary review and new
perspectives, Bulletin of Engineering Geology andEnvironment, Vol. 58, pp. 21-44.
Brenning, A. (2005): Spatial prediction models forlandslide hazards: review, comparison andevaluation, Natural Hazard and Earth System
Sciences, Vol. 5, No. 6, pp. 853-862.
Claessens, L., Schoorl, J.M., and Veldkamp, A.
(2007): Modelling the location of shallow landslides
and their effects on landscape dynamics in large
watersheds: an application for Northern New
Zealand, Geomorphology, Vol. 87, pp. 16-27.
Guzzetti, F., Carrara A., Cardinali M., and Reichenbach

6