Application of Weights-of-Evidence Model in Landslide

Application of Weights-of-Evidence
Model in Landslide Susceptibility
Mapping at Baozhong Region in Baoji,
China
Wei Chen
School of Resources and Earth Science, China University of Mining and
Technology, Xuzhou, Jiangsu 221116, China, e-mail: [email protected]
Wenping Li*
School of Resources and Earth Science, China University of Mining and
Technology, Xuzhou, Jiangsu 221116, China
*Communicating Author e-mail: [email protected]
ABSTRACT
The main purpose of this study was to produce landslide susceptibility mapping by
weights-of-evidence (WoE) model based on geographic information system (GIS) at
Baozhong region in Baoji, China. At first, a landslide inventory map was prepared using
earlier reports and aerial photographs as well as by carrying out field survey, and a total of
79 landslides were mapped and out of which 55 (70%) were randomly selected for building
landslide susceptibility model, while the remaining 24 (30%) were used for validating the
model. In this case study, the following landslide conditioning factors were evaluated: slope
degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from
faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility map
was produced using WoE model. Finally, the validation of landslide susceptibility map was
carried out using areas under the curve (AUC). The AUC plot estimation results showed that
the susceptibility map using WoE model has the success rate of 82.25%. Similarly, the AUC
plot showed that the prediction accuracy of the WoE model was 81.20%. According to the
validation results of the AUC evaluation, the map produced by WoE model exhibits a
satisfactory property. The landslide susceptibility map can be used for preliminary land use
planning and hazard mitigation purpose.
KEYWORDS:
Landslide; Susceptibility mapping; Weights-of-evidence (WOE);
Geographic information system (GIS); China
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INTRODUCTION
Landslides are a very common geomorphic hazard with considerable economic and
ecological consequences. Globally, landslides cause billions of dollars in damage and thousands
of deaths and injuries each year. Developing countries suffer the most as 0.5% of the Gross
National Product (GNP) per year has been lost due to landslides and 95% of landslide disasters
have been recorded in developing countries[1].
Generally, landslide susceptibility is defined as a quantitative and qualitative assessment of
the classification, volume (or area) and spatial distribution of landslides which exist or
potentially may occur in an area[2]. Landslide susceptibility mapping relies on a rather complex
knowledge of slope movements and their controlling factors. The reliability of landslide
susceptibility maps mainly depends on the amount and quality of available data, the working
scale, and the selection of the appropriate methodology of analysis and modeling[3]. Since the
1970s, Geographic Information System (GIS) technology has given the distinctive capability of
automating and analyzing a variety of spatial data. Over the decades, numbers of reports of
landslide analyses using GIS and statistical models such as logistic regression have been used in
landslide susceptibility mapping[4-25]. Other different methods such as weights of evidence
method[23, 26-31, 105-107], certainty factor (CF) model[22, 30, 32], analytical hierarchy process (AHP)[25,
32-44]
, frequency ratio (FR) model[10, 13, 23, 25, 45-47], statistical index (SI) model[44, 48], index of
entropy (IoE) model[22, 49], fuzzy logic[38, 43, 50-57], artificial neural network model[11, 15-16, 25, 52, 58-72],
neuro-fuzzy[73-78], support vector machine (SVM)[16, 78-81], decision-tree method[78, 82] have also
been applied for landslide susceptibility evaluation. All these models provide solutions for
integrating information levels and mapping the outputs.
The main purpose of this paper is to develop landslide susceptibility map of the Baozhong
region in Baoji, China (Figure 1), using the weights-of-evidence (WoE) model in landslide
susceptibility mapping in the study area. The model exploit information obtained from an
inventory map to predict where landslides may occur in future. To evaluate the accuracy of the
model, the assessed susceptibility levels in the study area were compared with data of past
landslides, and the model’s prediction capability was tested.
THE STUDY AREA
The study area covers roughly a surface area of 811.206 km2 between latitudes 34°17′ to 34°46′N, and
longitudes 106°48′ to 107°35′E, and is located in Chencang District of Baoji, China (Figure 1). The
Baozhong railway traffic line crosses the study area, and we call the study area as Baozhong region. The
altitude of the study area ranges from 520m to 2,060m. The study area is mainly distributed by loess and
79 landslides distributed in the study area. The average mean annual temperature is 12.9℃ while the mean
annual maximum and minimum temperatures range between 18.3℃ and 8.5℃, respectively. The mean
annual rainfall in the area varies from 700mm to 800mm. The main rainy months are from July to
September. The study area is formed by plains, loess tableland, loess bridge and hill, and rock mountains.
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LANDSLIDE CONDITIONING FACTORS
Landslide inventory
The mapping of existing landslides is essential to study the relationship between the landslide
distribution and the conditioning factors. A landslide inventory map is one that identifies the definite
location of the existing landslides along with its type and the time of occurrence[83-85]. The first step in
landslide susceptibility assessments is to acquire information about the landslides that have occurred in
the past. This stage is considered as the fundamental part of the landslide hazard studies[86-87]. Since
landslide occurrences in the past and present are keys to spatial prediction in future[86], a landslide
inventory map is a prerequisite for such a study. A landslide inventory map provides the basic information
for evaluating landslide hazards or risk. Accurate detection of the location of landslides is very important
for probabilistic landslide susceptibility analysis. In order to produce a detailed and reliable landslide
inventory map, extensive field surveys and observations were performed in the study area. A total of 79
landslides were identified and mapped by evaluating aerial photos in 1:50,000 scale with well supported
by field surveys and subsequently digitized for further analysis. The locations (centroid) of 79 landslides
are mapped in Figure 2. From these landslides, 55 (70%) randomly selected were taken for making
landslide susceptibility model and 24 (30%) were used for validating the model. The study area was
divided into a grid with 50×50m cell, occupying 984 rows and 1,146 columns.
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Figure 1: Location of the study area
Various thematic data layers representing landslide conditioning factors namely slope degree, slope
aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers and
precipitation were prepared. These factors fall under the category of preparatory factors, responsible for
the occurrence of landslides in the region for which pertinent data can be collected from available
resources as well as from the field.
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Slope degree
The main parameter of the slope stability analysis is the slope degree[88]. Because the slope degree is
directly related to the landslides, it is frequently used in preparing landslide susceptibility maps[58, 87, 89-92].
For this reason, the slope degree map of the study area is prepared from the digital elevation model
(DEM) and divided into six slope categories with an interval of 10°(Figure 3): 0-10°, 10-20°, 20-30°,
30-40°, 40-50°, and >50°.
Figure 2: Landslide inventory map
Slope aspect
Aspect is accepted as a landslide conditioning factor, and this parameter is considered in several
studies[58, 87, 93-94]. Some of the meteorological events such as the amount of rainfall, amount of sunshine,
and the morphologic structure of the area affect the propensity of landslides. The hillsides receiving dense
rainfall reach saturation faster; however, this is also related to filtering capacity of the slope controlled by
various parameters such as slope topography, soil type, permeability, porosity, humidity, organic
ingredients, land cover, and the climatic season. As a result, pore water pressure of the slope-forming
material changes[43]. In this study, the aspect map of the study area is produced to show the relationship
between aspect and landslides (Figure 4). Aspects are grouped into 9 classes such as flat (-1), north
(337.5°–360°, 0°–22.5°), northeast (22.5°–67.5°), east (67.5°–112.5°), southeast (112.5°–157.5°), south
(157.5°–202.5°), southwest (202.5°–247.5°), west (247.5°–292.5°), and northwest (292.5°–337.5°).
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Plan curvature
The term curvature is theoretically defined as the rate of change of slope gradient or aspect,
usually in a particular direction[95]. The curvature value can be evaluated by calculating the
reciprocal value of the radius of curvature of that particular direction[96]. Hence, while the
curvature values of broad curves are small, the tight ones have higher values. Plan curvature is
described as the curvature of a contour line formed by intersecting a horizontal plane with the
surface (Figure 5). The influence of plan curvature on the slope erosion processes is the
convergence or divergence of water during downhill flow[50, 43, 77]. For this reason, this
parameter constitutes one of the conditioning factors controlling landslide occurrence[43, 96].
The plan curvature map was produced using the GIS software of ArcGIS9.3.
Altitude
Altitude is another frequently used parameter for landslide susceptibility studies[43]. In the
study area, the elevation ranges between 520 and 2,060 m. The elevation values were divided
into six categories with an interval of 200m (Figure 6).
Geomorphology
Geomorphology is considered as an important factor closely related to landslide occurrence
because geomorphological units are created based on the analyses and integration of the
topological characteristics, geological structures, Neotectonic movements, and
morphometries[92]. Five geomorphological units can be identified in the study area (Figure 7) i.e.
mountain areas (22.40%), loess ridge and hill areas(40.45%), loess tableland areas(15.57%),
river terrace areas(10.47%), and plain areas (11.11%).
Lithology
The landslide phenomenon, a part of geomorphologic studies, is related to the lithology of
the land. Since different lithological units have different landslide susceptibility values, they are
very important in providing data for susceptibility studies. For this reason, it is essential to group
the lithological properties properly[7, 43, 97]. Therefore, the geological map of the study area was
prepared and was digitized in ArcGIS9.3. The study area is covered with various types of
lithological units. The general geological setting of the area is shown in Figure 8.
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Distance from faults
The distance from faults is calculated at 200m intervals using the geological map (Figure 9).
Euclidean distance method was applied, and a visual inspection was done to see the correlation
between the faults and landslides. Faults form a line or zone of weakness characterized by
heavily fractured rocks[98]. Generally speaking, farther the distance from tectonic structures will
result less numbers of landslides.
Distance from rivers
An important parameter that controls the stability of a slope is the saturation degree of the
material on the slope[39, 99]. The closeness of the slope to drainage structures is another important
factor in terms of stability. Streams may adversely affect stability by eroding the slopes or by
saturating the lower part of material resulting in water level increases[43, 100-101]. For this reason,
six different buffer zones were created within the study area to determine the degree to which the
streams affected the slopes (Figure 10). Euclidean distance method was applied, and a visual
inspection was done to see the correlation between the river and landslide.
Precipitation
Precipitation is closely associated with landslide initiation by means of its influence on
runoff and pore water pressure[100-104]. Researchers usually refer to one of the four main
characteristics of rainfall measures as factors of landslide initiation: (1) total rainfall, (2) short
term intensity, (3) antecedent precipitation, or (4) storm duration. Because, precise rainfall data
are scarce in this region, an average annual precipitation map of the study area was generated
and was divided into 2 classes: 700-750mm/year, 750-800mm/year (Figure 11).
LANDSLIDE SUSCEPTIBILITY MAPPING
Application of weights-of-evidence model
Weights-of-evidence (WoE) is a quantitative “data-driven” method which uses the
Bayesian probability model, and have been used in landslide susceptibility mapping by several
researchers[23, 26-31, 105-107]. Bayes’ probability theorem can be written as:
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Figure 3: Slope degree map of the study area
798
Figure 4: Slope aspect map of the study area
Figure 5: Plan curvature map of the study area
Figure 6: Altitude map of the study area
Figure 7: Geomorphology map of the study area
Figure 8: Lithology map of the study area
Figure 9: Distance from faults map of the study area
Figure 10: Distance from rivers map of the study area
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Figure 11: Precipitation map of the study area
P(A B ) =
P(B A)× P( A)
P (B )
(1)
By overlaying landslide locations with each evidence (conditioning factors), the statistical
relationship can be measured between them and assessed as to whether and how significant the
evidence is responsible for the occurrence of past landslides[27]. The WoE model is
fundamentally based on the calculation of positive and negative weights W + and W − . The
method calculates the weight for each landslide predictive factor (A) based on the presence or
absence of the landslides (B) within the area as follows[108]:
Wi + = ln
Wi − = ln
P{B D}
P{B D}
P{B D}
P{B D}
(2)
(3)
where P is the probability and ln is the natural log. Similarly, B is the presence of potential
landslide predictive factor, B is the absence of a potential landslide predictive factor, D is the
presence of landslide, and D is the absence of a landslide. A positive weight ( Wi + ) indicates that
the predictable variable is present at the landslide locations and the magnitude of this weight is
an indication of the positive correlation between the presence of the predictable variable and the
landslides. A negative weight ( Wi − ) indicates the absence of the predictable variable and shows
the level of negative correlation[106]. The standard deviation of W is calculated as:
S (C ) = S 2W + + S 2W −
(4)
where S 2W + is the variance of the positive weights and S 2W − is the variance of the negative
weights. In landslide susceptibility mapping, the weight contrast, C(C= Wi + − Wi − ), measures
and reflects the spatial association between the evidence feature and landslide occurrence. C is
positive for a positive spatial association and negative for a negative spatial association[107]. The
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studentized contrast is a measure of confidence and is defined as the ratio of the contrast divided
by its standard deviation[108].
To perform the WoE modeling, every parameter map is crossed with the landslide inventory
map using the ArcGIS 9.3 software, and the density of the landslide in each class is calculated.
The resultant weights for each thematic map for the WoE model are given in Table 1. These
weights were then analyzed by using the weighted sum option in the spatial analyst tools of
ArcGIS 9.3 to get the final LSI map (Figure 12). The final calculated LSI values of the study area
for WoE model range from about -14.87 to 30.22. Obviously, larger LSI values indicate a higher
susceptibility for landsliding. The index values were classified into five classes (very low, low,
moderate, high, and very high) using the natural break method. The areas in the very low, low,
moderate, high and very high landslide susceptibility classes are 20.85%, 29.95%, 20.91%,
17.51%, and 10.78%, respectively.
Table 1: Spatial relationship between each landslide conditioning factor and landslide
by WoE model
Factors
Classe
Perc
Per
W+
W−
C
S 2 (W + S 2 (W − S (C )
C / S (C
Slope
0-10
49.6
18.
-1.0
0.4
-1.4
0.1
0.0
0.3
-4.2
10-20
27.5
67.
0.8
-0.8
1.6
0.0
0.0
0.2
5.8
20-30
17.5
14.
-0.1
0.0
-0.2
0.1
0.0
0.3
-0.5
30-40
0.71
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
40-50
4.53
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
>50
0.01
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Flat
8.87
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
North
7.41
9.0
0.2
-0.0
0.2
0.2
0.0
0.4
0.4
Northe
12.0
20.
0.5
-0.0
0.6
0.0
0.0
0.3
1.7
Slope
Plan
Altitude(
Geomorp
East
12.7
14.
0.1
-0.0
0.1
0.1
0.0
0.3
0.4
Southe
13.8
10.
-0.2
0.0
-0.2
0.1
0.0
0.4
-0.6
South
17.0
20.
0.1
-0.0
0.2
0.0
0.0
0.3
0.5
South
12.6
7.2
-0.5
0.0
-0.6
0.2
0.0
0.5
-1.1
West
7.71
10.
0.3
-0.0
0.3
0.1
0.0
0.4
0.8
North
7.76
7.2
-0.0
0.0
-0.0
0.2
0.0
0.5
-0.1
Conca
39.4
32.
-0.1
0.1
-0.2
0.0
0.0
0.2
-1.0
Flat
15.2
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
Conve
45.2
67.
0.4
-0.5
0.9
0.0
0.0
0.2
3.1
<700
22.9
32.
0.3
-0.1
0.4
0.0
0.0
0.2
1.7
700-9
24.7
41.
0.5
-0.2
0.7
0.0
0.0
0.2
2.8
900-1
27.3
23.
-0.1
0.0
-0.1
0.0
0.0
0.3
-0.6
1100-
11.8
1.8
-1.8
0.1
-1.9
1.0
0.0
1.0
-1.9
1300-
7.29
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
>1500
5.83
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Mount
22.4
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
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Lithology
801
Loess
40.4
60.
0.3
-0.4
0.7
0.0
0.0
0.2
2.8
Loess
15.5
21.
0.3
-0.0
0.4
0.0
0.0
0.3
1.2
Terrac
10.4
14.
0.3
-0.0
0.3
0.1
0.0
0.3
0.9
Plain
11.1
3.6
-1.1
0.0
-1.2
0.5
0.0
0.7
-1.6
Loess
59.4
92.
0.4
-1.7
2.1
0.0
0.2
0.5
4.1
Silt
5.47
5.4
0.0
0.0
0.0
0.3
0.0
0.5
-0.0
Sand
10.3
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
Gluten
5.18
1.8
-1.0
0.0
-1.0
1.0
0.0
1.0
-1.0
Limest
1.87
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Meta
2.54
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Granit
15.1
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
Distance
0-200
5.20
21.
1.4
-0.1
1.6
0.0
0.0
0.3
4.9
from
200-4
5.13
1.8
-1.0
0.0
-1.0
1.0
0.0
1.0
-1.0
400-6
5.05
9.0
0.5
-0.0
0.6
0.2
0.0
0.4
1.3
600-8
4.91
5.4
0.1
-0.0
0.1
0.3
0.0
0.5
0.1
800-1
4.75
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
74.9
61.
-0.1
0.4
-0.6
0.0
0.0
0.2
-2.2
Distance
0-200
10.2
9.0
-0.1
0.0
-0.1
0.2
0.0
0.4
-0.2
from
200-4
9.82
21.
0.8
-0.1
0.9
0.0
0.0
0.3
2.8
400-6
9.10
12.
0.3
-0.0
0.3
0.1
0.0
0.4
0.9
600-8
8.04
10.
0.3
-0.0
0.3
0.1
0.0
0.4
0.7
800-1
6.88
7.2
0.0
0.0
0.0
0.2
0.0
0.5
0.1
55.8
38.
-0.3
0.3
-0.7
0.0
0.0
0.2
-2.5
Precipitati
700-7
63.9
74.
0.1
-0.3
0.5
0.0
0.0
0.3
1.6
on
750-8
36.0
25.
-0.3
0.1
-0.5
0.0
0.0
0.3
-1.6
Figure 12: Landslide susceptibility map derived from the WoE model
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Validation of the landslide susceptibility map
Landslide susceptibility map without validation is of little meaningful[109]. The landslide susceptibility
map derived by WoE model was tested using the landslide data sets that were used for model building
process as well as from those that were not used in model building process. For this, the total landslides
observed in the study area were split into 2 parts, 55 (70%) landslides were randomly selected from the
total 79 landslides as the training data and the remaining 24 (30%) landslides were kept for validation
propose.
Spatial effectiveness of the susceptibility map was checked by areas under the curve (AUC). The rate
curves were created, and their areas under the curve (AUC) were calculated. The rate explains how well
the model and controlling factors predict the landslide. The model with the highest AUC is considered to
be the best. In order to obtain the success rate curve and predictive rate curve for landslide susceptibility
map, the calculated landslide susceptibility index values of all pixels in the map were sorted in descending
order. Then, the ordered cell values were categorized into 100 classes with 1% cumulative intervals, and
classified landslide susceptibility index map was prepared by the software of ArcGIS 9.3.
The success rate result was obtained by comparing the landslide training data with the
susceptibility map (Figure 13a). AUC plot assessment result showed that the AUC value was
0.8225 for WoE model, and the training accuracy was 82.25%. The prediction rate result was
obtained by comparing the landslide validation data with the susceptibility map (Figure 13b).
AUC plot assessment result showed that the AUC value was 0.8120 for WoE model, and the
prediction accuracy was 81.20%.
From the results of the AUC evaluation, it is seen that both the success rate and prediction
rate curve show almost similar result, and the model employed in this study showed reasonably
good accuracy in predicting the landslide susceptibility of the study area.
100
80
70
60
50
40
30
20
AUC=0.8225
10
0
0
10
(b)
90
20 30 40 50 60 70 80 90 100
Landslide susceptibility index rank (%)
Cumulative percentage of
landslide occurrence (%)
Cumulative percentage of
landslide occurrence (%)
100
(a)
90
80
70
60
50
40
30
20
AUC=0.8120
10
0
0
10
20 30 40 50 60 70 80 90 100
Landslide susceptibility index rank (%)
Figure 13: AUC representing quality of the model
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CONCLUSIONS
In this study, the weights-of-evidence (WoE) model was used for landslide susceptibility
mapping. Nine conditioning factors were considered, i.e. slope degree, aspect, plan curvature,
altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation,
for which maps were derived using various GIS tools. The selection of the 9 conditioning
landslide factors was based on consideration of relevance, availability, and scale of data that was
available for the study area. Hence, the selection is relative and subjective, and can be improved
in future research. In this process, a total of 79 landslides were identified and mapped. Out of
which, 55 (70%) were randomly selected for generating a model and the remaining 24 (30%)
were used for validation proposes. In this study, five landslide susceptibility classes, i.e. very
low, low, moderate, high, and very high susceptibility for landsliding, were derived with natural
break method. The AUC plots showed that the susceptibility map produced by the WoE model
has the success rate of 82.25% and the prediction accuracy of 81.20%. This shows that the model
employed in this study showed reasonably good accuracy in predicting the landslide
susceptibility.
The increasing population pressure has forced people to concentrate their activities on steep
mountain slopes. Thus, to safeguard the life and property from landslides, the susceptibility map
can be used as basic tools in land management and planning future construction projects in this
area. The landslide susceptibility map produced in this study can be used for optimum
management by decision makers and land use planners, and also avoidance of susceptible
regions in study area. Also, it is worth mentioning that the similar method can be used elsewhere
where the same geological and topographical feature prevails.
ACKNOWLEDGEMENTS
Financial support for this work, provided by the National Natural Science Foundation of
China (Grant no. 41172290 and 40572160), are gratefully acknowledged.
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