IJERD – International Journal of Environmental and Rural Development (2015) 6-2

Research articleerd

Comparison of Semi-Quantitative and Statistical Regression
Models in Assessing Landslide Prone Areas in WahigInabanga Watershed, Bohol, Philippines
TOMAS D. REYES, JR.*Bohol Island State University, Bohol, Philippines
Email: tomseyer@yahoo.com / tomseyer@gmail.com
Received 3 March 2015

Accepted 10 November 2015(*Corresponding Author)

Abstract The study attempted to compare and evaluate the two landslide hazard assessment
models, semi-quantitative (index-based) and statistical regression (bivariate statistical analysis
and logit regression) in predicting landslide prone areas in Wahig-Inabanga Watershed, Bohol,
Philippines. This was performed by comparing the predictive power of each model based on thefrequency distribution of past landslide events. Findings revealed that the combined bivariate
statistical analysis and logit regression model outdone index-based method in predicting
landslide occurrences. Results indicated high prediction accuracy on statistical model greaterthan the 75% threshold level set for evaluation on both pooled moderate to very high hazard
zone and the combined high and very high hazard zone with accuracy values of about 83.82%
and 76.72%, respectively. Conversely, the semi-quantitative model failed to meet the accuracy
threshold. The study showed that statistical regression model, though relatively difficult toimplement, can be a better substitute to the most commonly used semi-quantitative method as adecision-support tool for watershed management and land use planning in relation to landslide
risk mitigation, reduction, adaptation, and management.
Keywords bivariate statistical analysis, landslide hazard, logit regression, semi-quantitative
methodINTRODUCTION
Several computer-based tools are found useful in landslide prediction, hazard assessment, and mapping
especially when these tools are made use in tandem with geographic information system (GIS). GIS
serves as an indispensable tool for mapping areas prone to unpredictable hazard events, particularly
landslides. One of the best advantages of using this technology is the possibility of improving hazard
occurrence models by evaluating results and adjusting the input variables (Lanuza, 2008).
There are several methods used in landslide hazard assessment. Ayalew et al., (2005) and Reyes(2014) briefly discussed each method and grouped them into three major categories: semi-quantitative,quantitative, and hybrid. According to Ayalew et al., (2005) and also cited by Reyes (2014), some of
the methods are simple, especially those which rely on subjective assessments. Others, however,depend on complex mathematical concepts and are difficult to understand. Some old approaches havelong disappeared, others underwent a sort of refinement, and new methods are always coming. Manyof the latest methods are not yet available in known commercial GIS packages either as built-infunctions or additional modules. Data, then, are usually transformed to external software products forcore analyses.
The semi-quantitative method is the collective process of index and overlay analysis, thus termed“index-based method”. It is also called as expert-driven (Zhu and Huang, 2006) in which expert
opinions make great difference and become the basis during assessing of the type and degree of any
natural hazard. In the Philippines, it is commonly used in provincial and municipal local governmentⒸISERD166

IJERD – International Journal of Environmental and Rural Development (2015) 6-2

units for disaster risk reduction and management and even recommended by the Department ofEnvironment and Natural Resources (DENR) to be a decision-support tool in forest management andconservation planning. A vulnerability assessment manual adopting this method has been prepared by
the Ecosystems Research and Development Bureau (ERDB) and made available for public use since2011.
On the other hand, the statistical regression model, uses logit regression to develop a functional
relationship between a process and factors inherent in them. The applications of this model in the fieldof slope instability have evolved as an important tool, with specific reference to landslide hazard
mapping. In landslide hazard mapping, an area is classified according to relative classes of instabilityon the basis of the degree of occurrence of landslide and mass movements (Jade and Sarkar, 1993).
In this study, both methods were applied for landslide hazard assessment in Wahig-Inabanga
Watershed, Bohol, Philippines to determine which between the two models is more appropriate inpredicting future landslide events based on the frequency distribution of past landslide occurrences.
METHODOLOGY
Landslide Hazard Mapping using Semi-quantitative (index-based) Method
The landslide hazard map prepared using the semi-quantitative method, particularly the index-based,
was completed following the procedures suggested by the ERDB-DENR in its vulnerability manual
published in 2011. It involved division of pre-defined landslide-related instability factors such as slope,soil type, rainfall, lithology, and land use into 5 classes using a set of criteria that influencevulnerability of the study area to landslide. These criteria were also used in assigning class ratings. The
most influential class trait was given the highest rating of 1, while the least influential was rated 0.
This was followed by overlaying of instability factors based on desired factor weights. Weights
used were 0.35 for slope, 0.20 for both rainfall and geology, 0.15 for land use, and 0.10 for soil type.
Landslide Hazard Mapping using Logit Regression (Combined Bivariate Statistical Analysis andLogit Regression) Model
The statistical regression model, same with the index-based method, also necessitated factor and classweighing. Bivariate statistical analysis was used to determine class weights, while logit regression
allowed the computation of factor weights. However, the logit regression, unlike semi-quantitative
method, required the utilization of landslide inventory or landslide occurrence map (van Westen, 1994
as cited by Wahono, 2010) to implement factor and class weighing. This means that factor and classweights are dependent on the landslide inventory and not on pre-defined vulnerability or susceptibility
criteria. To do this, the landslide inventory map was overlaid with nine significant landslide-related
instability parameters like elevation, slope, aspect, lithology, distance from fault line, distance fromrivers, distance from roads, rainfall, and land use. Landslide pixels laid on each class of instability
factors were computed as landslide frequencies. These frequencies served as class weights and wereused as class numerical values in logit regression. Important outputs of logit regression in SPSS
included regression coefficients of all parameter considered as factor weight and the model prediction
probability.
Details on how these two maps were generated are discussed in the DENR Vulnerability
Assessment Manual (ERDB, 2011) for the index-based method and the works of Reyes (2014),
Ayalew et al., (2005) and Ayalew and Yamagishi (2005) for the logit regression with bivariate
statistical analysis referred in their reports as quantitative method.

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Comparison of the Two Models
Model comparison was performed to determine which of the two approaches was more reliable inlandslide hazard prediction. Comparison was based on the frequency distribution of past landslide
events [=pixels] rested on the pooled upper moderate to very high hazard zones [P(Y=1) > 0.5 logit
regression default cut-off value] and the combined zone rated as high and very highly [P(Y=1) > 0.6]
prone to landslide occurrences using the 75% model prediction accuracy threshold. This was done by
applying the overlay and extract by sample function in spatial analyst tool of ArcGIS.
RESULTS AND DISCUSSIONLandslide Hazard Assessment
Semi-quantitative method: Table 1 presents the summary results of landslide hazard assessment
using the semi-quantitative method. Based on Table 1 and depicted in Fig. 1, the biggest part of thewatershed, about 71.50% or 44,540 ha, was predicted moderately prone to landslides. Considerable
areas had estimates of low (10,400 ha) and high (7,338) hazard ratings, while very small areas of thewatershed were estimated very low (4 ha) and very high (13 ha). From these results, it appears that thesemi-quantitative method overestimated the moderate landslide hazard zones and underestimated thevery low and very high landslide hazard areas. As shown in Fig. 1, most of the relatively flat areas in
the watershed fell within the moderate landslide hazard zone.
Table 1 Landslide hazard class ratings, area of coverage (ha) and percent distribution
generated using semi-quantitative method
Class RangeRating< 0.2
Very low
0.2-0.4Low
0.4-0.6
Moderate
0.6-0.8
High> 0.8
Very highTotal

Area (ha)410,400
44,5407,338
1362,295

Percent (%)
0.01
16.69
71.50
11.78
0.02
100

Table 2 Landslide hazard class ratings, area of coverage (ha) and percent distribution
generated using statistical regression model
Class Range< 0.2
0.2-0.4
0.4-0.6
0.6-0.8> 0.8Rating
Very low
Low
ModerateHigh
Very highTotal

Area (ha)38,18010,360
6,692
4,101
2,962
62,295Percent (%)
61.29
16.63
10.74
6.58
4.75
100

Statistical regression model: Table 2 shows the landslide hazard class ratings generated using thestatistical regression model, and their corresponding area (ha) and percent distribution. Results indicatethat more than 60% of the total area of the watershed (about 38,180 ha) was identified to have very low
probability of landslide occurrence. About 16.63% or 10,360 ha had low landslide hazard, whileroughly 6,692 ha or 10.74% was estimated to fall under the moderate landslide class. Conversely, highⒸISERD168

IJERD – International Journal of Environmental and Rural Development (2015) 6-2

and very high landslide ratings were predicted for areas mostly situated on the upper elevations of thewatershed (Fig. 2) having 4,101 ha and 2,962 ha, respectively.
The results show a decreasing area distribution against the increasing vulnerability of the area tolandslide.

Fig. 1 Landslide hazard map
generated using semiquantitative method

Fig. 2 Landslide hazard map
generated using the statisticalregression model

Table 3 Comparison of two models showing the frequency distribution of landslide pixels
in different hazard classes
HazardClass range

RatingStatistical regression
Frequency

Percent

Semi-quantitative
Frequency

Percent< 0.2

Very Low83

4.39

00.00

0.2-0.4

Low1246.5648

2.54

0.4-0.6

Moderate

232

12.27

1,005

53.15

0.6-0.8

High364

19.25

772

40.82

> 0.8

Very High
Total

1,088

57.54

66

3.49

1,891

100

1,891100

Method Comparison
Table 3 indicates the result of the landslide inventory layer and the landslide hazard maps overlay. It isnoticeable that there was a direct agreement between the landslide frequency (= number of pixels lying
on each hazard class) and the hazard zones for the statistical regression model, a characteristic of anideal method (Fig. 3). The highest landslide frequency of 1,088 or 57.54% was obtained from the very

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high landslide hazard zone. This was followed by high and moderate hazard zones with 364 (19.25%)
and 232 (12.27%), respectively.
Conversely, the distribution of landslide pixels was variable among the hazard zones of semiquantitative model, thus no relationship was observed. The result on Table 3 clearly shows that most of
the landslide pixels were found on moderate (1,005 or 53.15%) and high (772 or 40.82%) landslide
hazard zones, while only 66 pixels (3.49%) fell on very high hazard zone.

Fig. 3 Comparison of two models based on the percent distribution oflandslide pixels in different hazard classes
A process of combined zonation (combining classes) was also used to clearly evaluate thepredictive power of each model. The combined zone is referred to as unstable zone (=area) in the studyof Dhakal et al., (2000) such as the pooled upper moderate to very high hazard, and the high and very
high landslide hazard classes. Table 4 reveals the result of the model comparison based on thecomputed landslide frequency on these combined zones. Compared to the semi-quantitative method,
statistical regression model, at par, had higher prediction accuracy values of 83.82% and 76.72% [bothgreater than the 75% threshold level] based on the frequency and percentage of landslide events thatfall on moderate to very high [P(Y=1) > 0.5] and high and very high [P(Y=1) > 0.6] hazard zones,
respectively.
Table 4 Comparison of two different models showing the frequency distribution oflandslide pixels
Model
Semi-Quantitative
Statistical Regression

P(Y=1) > 0.5*
Frequency
Percentage
1,386
73.29
1,585

83.82

P(Y=1) > 0.6**
Frequency
Percentage
838
44.32
1,452

76.78

Note: * = default logit regression cut-off value in SPSS; upper moderate to very high hazard zone** = areas rated as high and very highly prone to landslideWith lower computed prediction accuracy, the semi-quantitative method, unfortunately, failed tomeet the threshold level set for acceptability which only means that this method is not suitable forlandslide hazard assessment and mapping particularly in Wahig-Inabanga Watershed. The statisticalregression model, then, becomes a better alternative method and substitute to semi-quantitative orindex-based method.

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CONCLUSION
Based on the findings of the study, it is concluded that the statistical regression method is a better
option to use when assessing landslide hazards in Wahig-Inabanga Watershed, Bohol, Philippines. The
advantage of applying a bivariate statistical analysis provided numerical values on instability factor
classes which were used in determining factor weights through logit regression. The idea of factor
weighing in logit regression is to find the best fitting function in defining the relationship between thepresence or absence of landslides and a set of landslide-related instability parameters. The objectivityof logit regression method in determining the significance of instability parameters in landslideprediction is wanting in semi-quantitative or index-based method.
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