Xueliang Wang I Luqing Zhang I Sijing Wang I Serena Lari
Regional landslide susceptibility zoning with consideringthe aggregation of landslide points and the weightsof factors
Abstract In this paper, we propose a methodology for landslidesusceptibility assessment at a regional scale in Yunnan, southwesternprovince of China. A landslide inventory map including 3,242 land-slide points was prepared for the study area. Five factors recognizedas correlated to landslide (namely, lithology, relative relief, tectonicfault density, rainfall, and road density) were analyzed and mappedin geographic information system. An index expressing the correla-tion between each factor and landslides [called class landslide sus-ceptibility index (CLSI)] was proposed in the study. While analyzinglandslide distribution in a large area, point aggregation might beexpected. To quantify the uncertainty caused by aggregation, classlandslide aggregation index was proposed. To account for the im-portance of each of the factors in the landslide susceptibility assess-ment, some weights were calculated by means of analytic hierarchyprocess. We propose a weighted class landslide susceptibility model(WCLSM), obtained by the combination of CLSI values of each factorwith the correspondent weight. WCLSM performance in the studyarea was evaluated comparing the results obtained by first modelingall landslides and then by performing a time partition. The modelwas run including only landslides that occurred before 2009 andthen validated with respect to landslides that occurred after 2009.The prediction–rate curve shows that the WCLSM model provides agood prediction for the study area. Of the study area, 21.4% showsvery high and high susceptibility and includes the 87.7% of thenumber of landslides that occurred after 2009.
Keywords Landslide susceptibility . Class landslide susceptibilityindex . Landslide point aggregation . Uncertainty . Validation
IntroductionLandslide susceptibility and hazard zoning have been developed sincethe 1970s to face practical problems at different scales (Cascini 2008).In recent years, some guidelines for landslide susceptibility, hazard,and risk zoning for land-use planning were proposed (Crosta andAgliardi 2003; Cascini 2008; Fell et al. 2008; Frattini et al. 2008).
In the literature, threemain approaches to landslide susceptibility,hazard, or risk assessment were proposed: heuristic, deterministic, andstatistical (Dai et al. 2002; Fall et al. 2006; Guzzetti et al. 2006; Frattiniet al. 2008; Ruff and Czurda 2008; Kouli et al. 2010). Heuristic ap-proaches are based on expert opinions to estimate landslide potential.They compare landslides with the related variables, with the assump-tion that the relationships between landslide susceptibility and thevariables are known and are specified in the models (Dai et al. 2002).
Deterministic approaches are based onmathematical expressionsof the correlation between causal variables and landslides, like me-chanical models for slope failure to predict landslides (Frattini et al.2004; Castellanos Abella and Van Westen 2008; Wang et al. 2012).These approaches are usually suitable at a local scale, but they aredifficult to use for a wide area, because mechanical parameters cannotbe extrapolated at a regional scale (Aleotti and Chowdhury 1999).
Statistical approaches combine the variables that have led tolandslide occurrence in the past in its statistical determination (Daiet al. 2002). They are particularly suited to determine landslide sus-ceptibility over large and complex areas (Cardinali et al. 2002). Suchapproaches provide quantitative estimates of “where” landslides areexpected, based on detailed information on the distribution of pastlandslides and a set of factors (Lan et al. 2004; Guzzetti et al. 2006).They can also compensate the loss of spatial resolution for the mix ofdifferent data sources (Ardizzone et al. 2002). Frequency ratiomodel isone kind of statistics-based approach widely used in landslide suscep-tibility analysis (Lee and Sambath 2006; Lee et al. 2007; Akgun et al.2008; Yilmaz 2009). For example, Lee et al. (Lee 2004; Lee and Sambath2006; Lee and Pradhan 2007; Lee et al. 2007) had done quite a fewanalyses of frequency ratio and got lots of significant achievements. Inthis study, we try to propose an approach for landslide susceptibilitythat is basically a modification of frequency ratio approach.
Generally, the purpose of landslide susceptibility mapping is tohighlight spatial distribution of potentially unstable slopes based ona detailed study of the causal factors for landslide (Ayalew et al.2005). However, the analysis of cause and effect relationships is notalways simple, and susceptibility or hazard maps in many occasionsattract heavy criticism. There are several reasons for this. For exam-ple, it is known that the preparation of landslide inventory mapsmainly come from aerial photos, satellite images, and topographicmaps (Ayalew et al. 2005; Frattini et al. 2008; Ruff and Czurda 2008;Yalcin 2008; Kouli et al. 2010). Factors such as the quality of aerialphotographs, the scale of topographic maps, the morphologicalcomplexity of the study area, and the type of land use or land covergreatly affect the reliability and completeness of landslide inventorymaps (Carrara et al. 1992; Ardizzone et al. 2002; Ayalew et al. 2005;Galli et al. 2008). The degree of experience of the persons who do therecognition of landslide features from different images and theefficiency of the work are also very important.
Landslide areas and their distribution can be obtained fromaerial photos when the study area is relatively small. If it covers alarge area, it would be more difficult to get a correct/precise inter-pretation from aerial photos. At the regional scale, landslides areusually recorded as points with longitude and latitude. However,while using a landslide inventory map based on points in suscepti-bility assessment, a significant uncertainty can be introduced. Actu-ally, differences or non-homogeneities exist in landslide identificationand recording criteria when data related to different areas come frommultiple sources. How to make reasonable use of the data to assesslandslide susceptibility is still a problem which needs to be solved(Buchin et al. 2011).
In this study, we propose an approach based on the defi-nition of a class landslide susceptibility index (CLSI), whichhas the aim of analyzing the correlation between correlatedfactors and landslides. The CLSI seems to be helpful to reducethe uncertainty in susceptibility assessment, particularly when
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a landslide inventory with non-homogeneity problems is used.We developed a semiquantitative weighted class landslide sus-ceptibility model (WCLSM) for landslide susceptibility assess-ment based on the calculation of CLSI corrected by weightsassigned to the correlated factors.
Study area and landslide inventoryYunnan province is located in the southwest of China (Fig. 1),covering about 385,000 km2. About 84 % of the region is moun-tainous; 10 % is occupied by plains and hills, and 6 %, by water.The elevation is higher in the northwest and lowers towardssoutheast (Fig. 1), with an average value of 2,000 m. Total popula-tion of the province is 45.96 million people (2010), representing3.35 % of the whole national population.
Because of the influence of the atmospheric circulation, theclimate is dry from continental monsoon in winter and wet frommarine monsoon in summer. There is a small annual thermal vari-ation, about 10–15 °C. The annual precipitation is 1,100 mm in mostparts of Yunnan, but it is extremely nonuniform in different seasonsand at different locations; 80–90 % of rainfall is concentrated amongrainy season, from May to October. Because of the specific morpho-logic setting, geology, and climate, landslides (especially rockslidesand rockfalls) are numerous in Yunnan. Landslide susceptibilitymapping in this study started with the preparation of a landslideinventory map represented by 3,242 landslide points, collected by,among others, the local Geology Survey Department.
Framework of landslide susceptibility zoningAs known, there are still no universal guidelines for selectingfactors for landslides susceptibility mapping (Ayalew et al. 2005).Large landslides, including rockslides, rock avalanches, and debrisflows, result from a complex of controlling features and processes(Hutchinson 1988). Crosta has summarized the most importantcausative parameters (Crosta and Clague 2006, 2009) includinggeological history (Ballantyne 2002), lithology and structure(Agliardi et al. 2001; Gutiérrez-Santolalla et al. 2005; Ambrosiand Crosta 2006), slope relief and shape (Savage et al. 1985; Savageand Swolfs 1986; Martel 2000; Molnar 2004), seismicity (Crosta etal. 2005), human activity (Heim 1932; Cruden 1976), and ground-water and drainage (Hovius et al. 1998).
Generally, the selection of correlated factors should take intoaccount the geological characteristics of the study area and dataavailability. In geographic information system (GIS)-based studies,any selected factor should be operational, complete, nonuniform,measurable, and nonredundant (Ayalew et al. 2005). The selection ofthe five correlated factors used in this study (namely, lithology,relative relief, tectonic faults, road network, and rainfall; Figs. 2, 3,4, 5, 6) is based on the literature (Crosta and Clague 2006, 2009) andstrongly controlled by data availability. Factor maps present a reso-lution of raster cell of 1×1 km. In the treatment process of the factors,various advanced GIS techniques (such as neighborhood statistics,density, kriging, vector to raster conversion, re-classification, andraster calculations) were applied.
Lithology mapLandslide phenomena are closely related to lithology and weatheringproperties of materials (Kouli et al. 2010; Lan et al. 2004). Thegeological strata exposed in the study area vary in age from Sinianto Quaternary. To simplify the high number of geological units, they
Fig. 2 The classed lithology map
Fig. 1 Elevation of Yunnan provinceand its location in China
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were classified in eight classes (Fig. 2) according to their geotechnicalbehavior with respect to landslides (Table 1).
Relative relief mapDigital elevation models (DEMs) are now standard tools for land-slide analyses (Kouli et al. 2010). In this study, we used a 90×90-mDEM obtained from International Scientific & Technical DataMirror Site, Computer Network Information Center, ChineseAcademy of Sciences (http://datamirror.csdb.cn).
Relative relief is the difference between the highest and thelowest elevation in an area (A). Tu and Liu (1990) studied therelative relief values affected by different As in different scales andpointed out that it is reasonable to define A as 21 km2 for anational- or regional-scale area study in China. Following theirexample and results, we analyzed the relative relief of Yunnanprovince for each 21 km2 area.
For the whole study area (385,645 cells), the relative reliefranges from 0 to 3,238 m. These values were divided into tenclasses to find their correlation with landslide occurrence. Classeswere distinguished using Jenks (1963) natural breaks in GIS. Thehighest number of cells can be found in classes 3 and 4 (126,112cells with relative relief values ranging from 503 to 818 m; Table 1).
Tectonic faultsTectonic structures, especially faults, represent favorable condi-tions for landslides. In some studies (Ruff and Czurda 2008), thedistance to faults was used to quantify their influence on land-slides. When the research covers a large area and many tectonicfaults are intersected mutually, the distance to fault is not suitableto be considered as a correlated factor. In this study, we proposedthe density of major tectonic faults (Fig. 4) as a predisposingfactor.
Different results are expected if different search radiuses areadopted in the analysis of fault density. If the search radius isdefined small, it is not possible to calculate fault density for theareas a little far from the faults. Still, there is no standard to followin defining the search radius for different scales studied. Aftertrying different radiuses, we define the search radius as 20 km inanalyzing fault density in this study. The density (in kilometer persquare kilometer) of major faults was divided into ten classesusing Jenks (1963) natural breaks in GIS (Table 1).
Rainfall mapThe precipitation data for a time period of 30 years (1979–2009)were collected from 35 rainfall stations in Yunnan province (Fig. 5).Considering the fact that 80 to 90 % of precipitation is concen-trated in the rainy season from May to October every year, themonthly average precipitation in rainy season was calculated foreach station. Based on it, a precipitation distribution map for thewhole area was obtained by a co-kriging interpolation (five neigh-bor points included) in GIS. The map was reclassified in ten classesusing Jenks (1963) natural breaks.
Road densityThe engineering constructions associated with road networkhave an important role in triggering landslides. In the study,road density was proposed as a factor to quantify the influ-ence of excavations on landslide susceptibility, consideringrailways, national roads, and provincial roads (Fig. 6). Thedensity map was reclassified in ten classes using Jenks (1963)natural breaks in GIS (Fig. 6). Similar to the analysis of faultdensity, we define the search radius as 20 km in analyzingroad density in this study.
Fig. 4 Tectonic faults and its classified density map
Fig. 3 Classes distribution and classified map of the relative relief
The class landslide susceptibility indexA landslide inventory is the basic data for many methods inascertaining landslide susceptibility (Carrara et al. 1995; Chungand Fabbri 2003; Guzzetti et al. 1999; Chau et al. 2004). The quality,reliability, and completeness of the landslide inventory control thequality of the resulting susceptibility assessment (Carrara et al.1992; Ardizzone et al. 2002; Galli et al. 2008). Landslide identifica-tion and mapping are subjected to errors and uncertainties whichdepend on the skills of the surveyor and the technical toolsselected (Carrara et al. 1992; Ardizzone et al. 2002; Ayalew et al.2005). The uncertainty produced in the process of landslide iden-tification and mapping would propagate to the final landslidesusceptibility assessment.
In this study, the classified maps (Figs. 2–6) and the landslideinventory map were overlapped in GIS. The total number of cellsin each class, the number of cells intersected by landslides in eachclass, and the number of landslide points in each class werecounted, respectively.
Using the landslide inventory map, the quantification of land-slide susceptibility for each class of a factor could be defined bythree aspects as follows:
& The number of landslide points in the class;
& The total number of cells, i.e., the area of the class;
& The degree of spatial aggregation of points in different cells ofthe class.
The number of landslide points in a class directly shows thehistorical incidence of landslide in history, but it is also affected bythe number of cells belonging to the class. The point density ineach class (PCd) was defined as the number of landslide points inthe class divided by the total number of cells of the class.
However, it is still unsuitable to use only PCd to quantifylandslide susceptibility of the class, because of the aggregationprone to occur in the landslide inventory map. While analyzinglandslide distribution in a large area, point aggregation might beexpected, due to the natural clustering of phenomena. However,the aggregation usually occurs in actual practice. It comes fromdifferent knowledge backgrounds of surveyors on landslides, dif-ferent criteria in identifying and recording size and type of land-slides, different survey accurateness in different regions, anddifferent details in historical records of landslides. The spatialdistribution of the aggregation can be similar to that related tonatural clustering, but it shows more abrupt changes in patternsstrictly connected with different survey areas. It would be a com-plex issue to distinguish the aggregation from natural landslideclustering. This is not discussed in this paper. Besides, classifyingeach factor map into different classes and choosing the cell size areoperations which affect susceptibility assessment. The criteria toclassify maps and to choose the size of the cells are still difficult todefine, being strongly connected with the scale of the analysis,which can vary significantly in a susceptibility assessment. As anexample, for large areas, due to the low precision, scale, andresolution of the maps of the factors, large size of raster cells areusually chosen. This can lead to a large number of landslide pointsfalling within the same cell.
This is shown in Fig. 7, where the research area is divided inthe same number of cells having an area equal to a. Six of thembelong to class A for one specific factor. In both cases, 12 landslidepoints are included in class A, but the degree of their spatialaggregation in each case is different. In both situations, PCd is 12/6∙a. But in case 1, there is only one cell showing all landslideoccurrences. In case 2, the six cells show the same number oflandslides, which means the same landslide susceptibility. To take
5 Metamorphic rocks (mainly slate and metamorphosedsandstone)
1.80 0.014 0.025
6 Sediment rock (mainly sandstone) 0.93 0.0073 0.0068
7 Soft sediment rock (mudstone, shale) 0.88 0.0069 0.0061
8 Soil and sands 0.91 0.0072 0.0065
Relativerelief
1 0~326 m 0.16 0.0013 0.00020
2 327~502 m 0.37 0.0030 0.0011
3 503~661 m 0.66 0.0052 0.0034
4 662~818 m 0.94 0.0074 0.0069
5 819~983 m 1.35 0.011 0.014
6 984~1,164 m 2.58 0.021 0.054
7 1,165~1,375 m 1.25 0.0096 0.012
8 1,376~1,630 m 0.93 0.0071 0.0066
9 1,631~1,950 m 0.64 0.0048 0.0031
10 1,951~3,238 m 0.42 0.0036 0.0015
Fault density 1 0~0.055 km/km2 0.98 0.0079 0.0077
2 0.056~0.14 km/km2 0.87 0.0070 0.0061
3 0.15~0.22 km/km2 1.04 0.0083 0.0087
4 0.23~0.29 km/km2 0.98 0.0080 0.0079
5 0.30~0.36 km/km2 0.94 0.0074 0.0069
6 0.37~0.44 km/km2 0.92 0.0076 0.0070
7 0.45~0.52 km/km2 1.32 0.0095 0.013
8 0.53~0.62 km/km2 1.41 0.0097 0.014
9 0.63~0.74 km/km2 1.18 0.0089 0.011
10 0.75~1.08 km/km2 1.81 0.014 0.025
Rainfall 1 573.78~976.46 mm 0.25 0.0021 0.00051
2 976.47~1,224.26 mm 0.91 0.0070 0.0064
3 1,224.27~1,379.13 mm 0.64 0.0052 0.0033
4 1,379.14~1,544.33 mm 0.93 0.0067 0.0063
5 1,544.34~1,730.18 mm 2.09 0.017 0.035
6 1,730.19~1,947.01 mm 0.78 0.0064 0.0050
7 1,947.02~2,153.51 mm 1.18 0.0096 0.011
8 2,153.52~2,390.99 mm 0.86 0.0069 0.0059
9 2,390.10~2,711.06 mm 0.50 0.0038 0.0019
10 2,711.07~3,206.67 mm 0.18 0.0019 0.00034
Road density 1 0~0.015 km/km2 0.88 0.0071 0.0063
2 0.016~0.031 km/km2 1.05 0.0084 0.0087
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into account the aggregation, the susceptibility of class A in case 1should be lowered.
To quantify the uncertainty caused by aggregation, a classlandslide aggregation index (CLAI) was calculated for each class,as follows:
CLAI ¼ NL
N Tð1Þ
where, NL is the number of cells interested by landslide points, andNT is the total number of cells in the class.
The smaller the CLAI is, the more aggregated the landslidepoints are in a class. To reduce the uncertainty of aggregation inthe susceptibility assessment process, PCd was multiplied by CLAI.In this way, the influence of unreasonable high value of PCd for theclasses with high aggregated points was considered. Class landslidesusceptibility index was used to assess the susceptibility of eachclass calculated by Eq. (2). Actually, the item “PCd/PTd” representthe concept “frequency ratio” (Lee 2004). CLSI is a kind of adjust-ment of frequency ratio by introducing CLAI.
CLSI ¼ P Cd
P Td� CLAI ð2Þ
Where PCd is a point density in each class, PTd is a point densityin total study area, and CLAI is the class landslide aggregation index.
In general, regional landslide predictive models attempt toidentify where landslides may occur on the basis of a set ofcausal factors. The assumption is that slope failures in thefuture will be more likely to occur under the conditions whichled to past and present slope failure (Varnes and IAEG Inter-national Association for Engineering Geology 1984; Carrara etal. 1995). In this study, past landslides in each class of eachfactor are expressed by CLSI (Table 1), and it is used as animportant parameter to predict the landslide susceptibility inthe future.
Weights of factorsA variety of techniques is available to estimate factor weights. Acomprehensive description about analytic hierarchy process(AHP) can be found in Saaty (1980) and Saaty and Vargas(2001). The AHP methodology consists in the pairwise compar-ison of all possible pairs of factors. The approach requires theexpression of the dominance of one factor with respect to eachof the others in a semantic 1 to 9 scale (Table 2) and tosynthesize the judgments to determine the weights (Saaty andVargas 2001; Yalcin 2008).
In this study, it was difficult to assign a dominance score topairs of factors, due to the dimensions of the study area and tothe consequent variety of specific site characteristics. For thisreason, the relative rating for the dominance between each pairof factors was guided by expert knowledge. A comparison
Table 1 (continued)
Data layers Number of theclass
Classes PCd/PTd CLAI CLSI
3 0.032~0.045 km/km2 1.30 0.0010 0.013
4 0.046~0.060 km/km2 0.99 0.0078 0.0078
5 0.061~0.074 km/km2 1.03 0.0079 0.0082
6 0.075~0.093 km/km2 0.59 0.0046 0.0027
7 0.094~0.12 km/km2 0.64 0.0054 0.0035
8 0.13~0.15 km/km2 0.28 0.0022 0.00063
9 0.16~0.21 km/km2 0.63 0.0059 0.0037
10 0.22~0.26 km/km2 0.51 0.0044 0.0022
Fig. 7 Two cases showing different degree of concentration of landslide points in the cells belonging to class A, in a factor map
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matrix of scores was created (Table 3). The AHP approachrequires the computation of the eigenvector relative to themaximum eigenvalue for the matrix of the scores. The eigenvec-tor contains the weights for each factor rescaled between 0 and 1(Table 3).
AHP allows to check the internal coherence of both theexpert's attributions and the comparison matrix, through thecalculation of the eigenvalues and of the consistency ratio (CR,value ranging from 0 to 1), expressing the internal coherence ofthe attributions of each expert.
In this study, the value of CR is obtained by the ratiobetween the values of the indexes CI [matrix's consistency index,whose expression is shown in Eq. (3)] and a random index RI,which is the average consistency index. Its value equals 1.12 for a5×5 matrix of scores (Ayalew et al. 2004).
CI ¼ λ max �mm � 1
ð3Þ
where λmax is the maximum eigenvalue of comparison matrix,and m is the number of factors (five in this study).
A CR lower than 0.1 would be acceptable (Ayalew et al.2004), although this depends on the objective of the study.The value of CR in this study (0.02, Table 3) indicates theadequate degree of consistency of the comparison matrix.
Results and validationVarious approaches have been proposed in the literature to gen-erate landslide susceptibility maps. Examples can be found in(Varnes and IAEG International Association for Engineering Ge-ology 1984), Hartlen and Viberg (1988), Van Westen (1994),Mantovani et al. (1996), Ayalew et al. (2004), and Frattini et al.(2008). Referring to the concept “Landslide Susceptibility Index”
(Lee et al. 2007), we express landslide susceptibility by means of aweighted class landslide susceptibility index (WCLSI), whoseequation is shown as follows:
WCLSI ¼X5
1
Weight� CLSI ð4Þ
The classification of WCLSI values in different susceptibilitylevels was not easy, as there are no statistical rules guiding this step(Ayalew et al. 2005). Some mathematical methods (e.g., thoseproposed by Scott 1979 and Friedman and Diaconis 1981) allowto find the optimum bin width for the classification, but they areineffective in the case of multimodal distributions, as in this case(Suzen and Doyuran 2004). The problem of changing continuousdata into categories remains unclear in landslide susceptibilitymapping, because most authors use their expert opinions to de-velop class boundaries (Ayalew et al. 2005). In this study, fivesusceptibility classes are defined by means of Jenks (1963) naturalbreaks method in GIS (Ruff and Czurda 2008): very low suscepti-bility (33.3 % of the whole area), low susceptibility (24.1 % of thewhole area), medium susceptibility (22.8 % of the whole area), highsusceptibility (8.6 % of the whole area), and very high susceptibil-ity (11.2 % of the whole area) (Fig. 8).
In prediction modeling, validation of the prediction results isthe most important component (Chung and Fabbri 2003; Lee et al.2007). In the last decades, in spite of a large production of models,little attention has been devoted to the problems of results evalu-ation (Guzzetti et al. 2006; Frattini et al. 2010). After obtaining aprediction, the proper validation should be based on the compar-ison between the prediction results and the areas affected by futurelandslides (Chung and Fabbri 2003). To obtain an independentsample of landslides, different approaches including random,
Table 2 Scale of preference between two factors in AHP (Saaty and Vargas 2001)
Score Degree of preferences Explanation
1 Equally Two activities contribute equally to the objective.
3 Moderately Experience and judgment slightly to moderately favor one activity over another.
5 Strongly Experience and judgment strongly to essentially favor one activity over another.
7 Very strongly An activity is strongly favored over another and its dominance is shown in practice.
9 Extremely The evidence of favoring one activity over another is of the highest degree possible of an affirmation.
2, 4, 6, 8 Intermediate values Used to represent compromises between the preferences in weights 1, 3, 5, 7, and 9.
Reciprocals Opposites Used for inverse comparison
Table 3 Pairwise comparison matrix of scores for calculating weights
Relative relief Rainfall Fault density Road density Lithology Factor weights
Relative relief 1 3 5 7 3 0.4671
Rainfall 1/3 1 7 5 3 0.2613
Fault density 1/5 1/7 1 1 1/3 0.0575
Road density 1/7 1/5 1 1 1/5 0.0509
Lithology 1/3 1/3 3 5 1 0.1632
Consistency ratio (CR) 0.02
Landslides 11 & (2014) 405
space, and time partitions can be used. The last one is the mostadequate to test the validity of the prediction, but also the mostdifficult to apply (Remondo et al. 2004).
In this paper, we use 726 landsides that occurred after2009 in Yunnan province to validate the prediction results.The time partition for the validation is performed as follows(Chung and Fabbri 2003):
& Build the prediction model (WCLSM) and generate an overallprediction (Fig. 8) using all the past landslides (3,242) over thestudy area.
& Partition the data into two subsets by the year 2009: 2,516landslides points prior to 2009 and the remaining ones (726)that occurred after 2009 (Fig. 9).
& Run the same model (WCLSM) including only the landslidesthat occurred before 2009.
& Obtain statistics, e.g., the prediction–rate curve (Fig. 11), bycomparing the prediction results of the model based on land-slides that occurred before 2009 with the distribution of theoccurrence of landslides that occurred after 2009.
A prediction–rate curve was obtained by comparing the re-sults of WCLSM including only the landslides that occurred before2009 with the occurrences of landslides after 2009. This curve wasused for the evaluation of the modeling performance of the overallmodel (Chung and Fabbri 2003). The prediction–rate curve of themodel considering only landslides that occurred before 2009(Fig. 11) shows a good performance in prediction; 21.4 % of thestudy area is classified as presenting a very high and high suscep-tibility, and it includes 87.7 % of the total number of landslides thatoccurred after 2009.
DiscussionThe paper proposed a class landslide susceptibility index to ac-count for the aggregation of landslides in inventory maps. Theaggregation may come from different knowledge backgrounds ofsurveyors on landslides, different criteria in identifying and re-cording size and type of landslides, and different survey details indifferent regions and in unhomogeneous historical records oflandslides. Moreover, defining the number of the classes for eachfactor and their limiting values also introduces some uncertainties.Introducing CLSI in the model reduces both these types of un-certainties.
We compared the results obtained by including CLAI in themodel with those obtained without correcting aggregation. The fivefactormaps (Figs. 2–6)were reclassified using PCd/PTd values (Table 1)and weighted (Table 3). The landslide susceptibility map obtainedexcluding the use of CLAI is shown in Fig. 10. For validation, we usedthe same procedure mentioned above. It can be seen that the predic-tion–rate curve obtained without correcting aggregation (Fig. 11, redline) shows a lower prediction performance than the one based onCLSI (Fig. 11, pink line). The 11.3 % of the study area classified ashaving a very high susceptibility includes 57.0 % of the number oflandslides that occurred after 2009.
Weights have an important effect on the definition of the distri-bution and extension of each susceptibility class and on the predic-tive ability of the model. Using the same weight value for each factor(0.2), another landslide susceptibility map was created (Fig. 12), withthe related prediction–rate curve (Fig. 11, blue line). In this case, theprediction–rate curve shows a lower prediction performance. Actu-ally, the role applying the weights with the same value on CLSI is thesame as summing CLSI without applying weights. In other words,applying weights on adjusted frequency ratio (CLSI) would givemorereasonable prediction for landslide susceptibility than simply sum-ming CLSI in this case study.
The reliability and accurateness of a landslide susceptibility mapdepends on the factors selected for the analysis and on the method
Fig. 8 Landslide susceptibility map obtained by Eq. (4) and landslide inventorymap (3,242 points)
Fig. 9 Time partition of landslide points prior and following 2009
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used in susceptibility assessment. However, because of the lack ofunivocal criteria to prove the relevance of a factor in controllinglandslide occurrence, in this paper, the five factors were selectedbased on their apparent influence on landslide incidence. The detec-tion of the factors controlling landslide occurrence would improvethe modeling results greatly. The approach to select and test thelandslide-controlling factors would be studied in the future. For thispurpose, a model including other factors could also be run andcompared with the previous ones (e.g., including the elevation, slopeangle, aspect, terrain curvature, and frequency of rainfall). The
definition of weights greatly affects the prediction of a model. Incase-weighting strategies based on expert knowledge, a deep knowl-edge of the territorial context and of landslide occurrences in thestudy area is required.
The reliability of the model here used is strongly dependent onthe number of available landslide points. The inventory map used inthis case does not include all the landslides occurred in the past in thestudy area. For this reason, the model could be improved ifsupported by a more complete inventory map, in a further study.
ConclusionsIn this study, five factors correlated to landslide susceptibility (name-ly, lithology, relative relief, tectonic fault density, rainfall, and roaddensity) were considered. While analyzing landslide distribution in alarge area, point aggregation might be expected, due to the naturalclustering of phenomena. To account for the uncertainty caused bylandslide point aggregation, class landslide susceptibility index cal-culated based on frequency ratio and class landslide aggregationindex were proposed and used to express the correlation betweenthe factors and landslide occurrence. The model WCLSM proposedin this study for the landslide susceptibility assessment is the com-bination of CLSI and the weights assigned for each factor. Thevalidation of the model shows that CLSI represents a suitable indexto be used while modeling landslide susceptibility, particularly whenthe landslide inventory map presents point aggregation.
Although the result of the approach is partly subjectivedepending on the knowledge of experts (e.g., in weights attributionprocess performed by means of AHP), the semiquantitative methodhere described is shown to be useful while working at the regionalscale. In this study, Yunnan province was classified into five classes of
0
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0 10 20 30 40 50 60 70 80 90 100
Cumulative percentage of study area in susceptibility classes (%)
)%(
ecnaruccoedilsdnal
foegatnecrep
evitalumu
C
same value for the weight
based on the CSI proposed this study
without correcting for point aggregation
Fig. 11 Prediction–rate curves for the landslide susceptibility model
Fig. 12 Landslide susceptibility map (same weight for each factor, 0.2) and thelandslide inventory map (3,242 points)
Fig. 10 Landslide susceptibility map (without including CLAI) and the landslideinventory map (3,242 points)
Landslides 11 & (2014) 407
landslide susceptibility: very low susceptibility zone (33.3 % of thewhole area), low susceptibility zone (24.1 %), medium susceptibilityzone (22.8 %), high susceptibility zone (8.6 %), and very high sus-ceptibility zone (11.2 %). The susceptibility map obtained in this waycould be a helpful tool for regional land use planning in Yunnanprovince.
AcknowledgmentsThe authors would like to thank the support of the NationalTechnology Support Project (2008BAK50B04) and the ChineseAcademy of Sciences Knowledge Innovation Project importantdirection project (KZCX2–YW–Q03), and the National NaturalScience Foundation of China (40502027). The first author wishesto thank the China Scholarship Council for funding his stay atUniversity of Milan-Bicocca and the supervision by G. B. Crosta, P.Frattini, and F. Agliardi. The authors wish to express their appre-ciation to three anonymous reviewers, whose detailed commentswere very helpful in improving the manuscript.
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X. Wang ()) : L. Zhang : S. WangKey Laboratory of Engineering Geomechanics, Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029, Chinae-mail: [email protected]
S. LariDipartimento di Scienze Geologiche e Geotecnologie,Università degli Studi di Milano-Bicocca,Milano, Italy
