6 Landslide Susceptibility Modeling: Optimization and Factor Effect Analysis Biswajeet Pradhan and Maher Ibrahim Sameen 6.1 Introduction Landslides are considered devastating natural geohazards worldwide; they pose significant threats to human life and result in socioeconomic losses in many countries (Mahalingam et al 2016) A literature search shows that considerable efforts have been exerted to develop new ideas and tools that can improve the mitigation of landslide effects One field that is attracting the attention of an increasing number of researchers worldwide is landslide susceptibility modeling (LSM) LSM is the basic information required for hazard and risk assessments; it is also a critical component in disaster management and mitigation (Pradhan and Lee 2009; Bui et al 2015; Gaprindashvili and van Westen 2016) Significant studies on landslide susceptibility mapping were conducted in the last decades, thereby creating new ideas and research directions for future studies The optimization of landslide conditioning factors (Jebur et al 2014), the study of the effects of landslide sampling procedures (Hussin et al 2016), the development of novel and hybrid models (Moosavi and Niazi 2015), and the analysis of the effects of landslide factors (Guo and Hamada 2013) are among recent and significant research directions in landslide susceptibility studies Landslides are triggered by several factors that create challenges for researchers in analyzing and predicting different types of landslides In general, geomorphological, topographical, geological, and hydrological factors are among the factors that are widely studied and considered in LSM (Pradhan 2013; Pereira et al 2013) However, landslide conditioning factors, such as slope, aspect, land use, distance to road, and vegetation density are not consistent among studies In addition, the quality and quantity of data can also vary, thereby affect the accuracy of LSM Therefore, a detailed analysis and comprehensive investigation of the input data before LSM is performed are important to B Pradhan (&) Á M.I Sameen Department of Civil Engineering, University Putra Malaysia, Serdang, Malaysia e-mail: biswajeet24@gmail.com © Springer International Publishing AG 2017 B Pradhan (ed.), Laser Scanning Applications in Landslide Assessment, DOI 10.1007/978-3-319-55342-9_6 increase the accuracy of landslide susceptibility models In addition, recent advances in light detection and ranging (LiDAR) technology enable landslide researchers to collect high-quality data (Kasai et al 2009) Nevertheless, challenges remain because of the variability in topography and other conditions of different study areas Several studies have attempted to provide insights into landslide conditioning factors and have investigated these factors for LSM Mahalingam et al (2016) evaluated landslide susceptibility mapping techniques using LiDAR-derived factors in Oregon City The results of their study showed that only a few factors were necessary to produce satisfactory maps with a high predictive capability (area under the curve >0.7) Qin et al (2013) investigated uncertainties caused by digital elevation map (DEM) error in LSM The uncertainty assessment showed that modeling techniques could have varying sensitivities to DEM errors Mahalingam and Olsen (2015) assessed the influences of the source and spatial resolution of DEMs on derivative products used in landslide mapping Their study showed that a fine resolution would not necessarily guarantee high predictive accuracy in landslide mapping, and the source of the datasets would be an important consideration in LSM The effects of landslide conditioning factor combinations on the accuracy of LSM were explored by Meten et al (2015) In their study, the accuracy of LSM was improved by removing certain landslide conditioning factors based on their correlations with other factors Kayastha (2015) conducted a study on factor effect analysis using the frequency ratio (FR) model in Nepal The results indicated that using all nine causative factors produced the best success rate accuracy of over 80% However, in the study of Vasu and Lee (2016), an LSM with 13 relevant factors selected from the initial 23 factors presented a success rate of 85% and a prediction rate of 89.45% Hussin et al (2016) evaluated the effects of different landslide sampling procedures on a statistical susceptibility model The study demonstrated that the highest success rates were obtained when sampling shallow 115 116 landslides as 50 m grid points and debris flow scarps as polygons The highest prediction rates were achieved when the entire scarp polygon method was used for both landslide types The sample size test using the landslide centroids showed that a sample of 104 debris flow scarps was sufficient to predict the remaining 941 debris flows, whereas 161 shallow landslides were the minimum number required to predict the remaining 1451 scarps The current study used 15 landslide conditioning factors and an adequate number of landslide inventories to investigate the optimization of landslide conditioning factors and conduct a factor effect analysis for developing landslide susceptibility models in the Cameron Highlands, western Malaysia After multicollinearity and factor effect analyses were performed, Ant colony optimization (ACO) was utilized to select significant landslide conditioning factors among the initial 14 factors for further analysis Data mining techniques, including support vector machine (SVM) and random forest (RF), were used to analyze the effects of the selected landslide conditioning factors on the prediction rate accuracy of the susceptibility models Details and discussions on the obtained results are presented in the remainder of this chapter B Pradhan and M.I Sameen 6.2 Study Area and Landslide Inventory Data The Cameron Highlands is a tropical rain forest district located in western Malaysia at the northwestern tip of Pahang It is approximately 200 km from Kuala Lumpur Previous studies have reported several landslides in this region, which have caused significant damages to properties (Khan 2010) The lithology of the Cameron Highlands mainly consists of Quaternary and Devonian granite and schist (Pradhan and Lee 2010) The granite in the Cameron Highlands is classified as megacrysts biotite granite (Pradhan and Lee 2010) A subset that occupies a surface area of approximately 25 km2 was selected for the current study because of the frequent occurrence of landslides in this area (Fig 6.1) The lowest and highest altitudes are 889.61 and 1539.49 m, respectively Multisource remote sensing images and geographic information system (GIS) data were used to collect and prepare a landslide inventory database for LSM Remote sensing data, including archived 1: 10,000–1: 50,000 aerial photographs, SPOT panchromatic satellite images, and high-resolution LiDAR-based orthophotos, were used to Fig 6.1 Geographic location of the study area and the landslide inventory map created by using multisource remote sensing data Landslide Susceptibility Modeling … visually detect landslide occurrences in the study area In addition, all historical landslide reports, newspaper records, and archived data for the period under examination were collected The locations of the individual landslides were drawn on 1:25,000 maps based on the site description, archived database, and aerial photograph interpretation Field observations were performed to confirm fresh landslide scarps In the aerial photographs and SPOT satellite images, historical landslides could be observed as breaks in the forest canopy, bare soil, or geomorphological features, such as head and side scarps, flow tracks, and soil and debris deposits below a scarp These landslides were then classified and sorted based on their modes of occurrence Most of the landslides are shallow rotational, whereas a few are translational A few landslides that occurred in flat areas were not considered, and thus eliminated from the analysis To create a database for assessing the surface area and number of landslides in the study area, landslides were mapped within an area of 25 km2 The landslide inventory map is shown in Fig 6.1 6.2.1 Preparation of Landslide Conditioning Factors A geospatial database that contained 15 landslide conditioning factors was prepared for susceptibility analysis in GIS Some factors were derived from a LiDAR-based DEM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, whereas others were digitized from GIS layers collected from government agencies First, a DEM at 0.5 m spatial resolution was created from LiDAR point clouds using a multiscale curvature algorithm and inverse distance weighted (IDW) interpolation techniques implemented in ArcGIS 10.3 Subsequently, slope, aspect, profile, and plan curvature were derived from the generated DEM at 0.5 m spatial resolution using the spatial analysis tools of GIS In the case of curvature, negative curvatures represent concave surfaces, zero curvatures represent flat surfaces, and positive curvatures represent convex surfaces In addition, four hydrological factors, namely the topographic wetness index (TWI), the topographic roughness index (TRI), the stream power index (SPI), and the sediment transport index (STI), were derived from the slope and flow accumulation layers The land cover map was prepared from SPOT satellite images (10 m spatial resolution) using a supervised classification method The map was verified via field survey Then, 10 classes of land cover types were identified, including water bodies, transportation, agriculture, residential, and bare land The normalized difference vegetation index (NDVI) map was generated from SPOT satellite images (10 m spatial resolution) The NDVI value was 117 calculated using the formula NDVI = (IR − R)/(IR + R), where IR and R denote the energy reflected in the infrared and red portions, respectively, of the electromagnetic spectrum Finally, distance to road, distance to river, and distance to lineament were calculated based on the Euclidean distance method using the GIS layers Several studies have explained the contributing factors of a landslide The significance of a particular factor depends on site-specific conditions In the current study, soil and lithology were not used because the study area consists of only one type of soil and lithology However, 15 factors were used, namely altitude, slope, aspect, profile curvature, plan curvature, land use, TWI, TRI, SPI, STI, NDVI, vegetation density, distance to road, distance to river, and distance to the fault The succeeding paragraphs briefly describe these factors Altitude is controlled by several geological and geomorphological processes Landslides typically occur at intermediate elevations because slopes tend to be covered by a layer of thin colluvium, which is prone to landslides In this study, the lowest and highest altitudes were 889.61 and 1539.49 m, respectively The altitude layer was reclassified into six classes using the quantile classification method, as shown in Fig 6.2d The slope is a measure of the rate of change in elevation in the direction of the steepest descent and is considered the main cause of landslides The slope gradient map of the study area was divided into six slope angle classes The study area has flat regions The highest slope was observed at 80° (Fig 6.2e) Aspect is defined as the slope direction measured (in degrees) from the north in a clockwise direction It ranges from 0° to 360° Parameters, such as exposure to sunlight, rainfall, and dry winds control the concentration of soil moisture, which in turn, determines landslide occurrence (Fig 6.2f) Plan curvature is described as the curvature of a contour line formed by the intersection of a horizontal plane with the surface It influences the convergence and divergence of flow across a surface Profile curvature, in which the vertical plane is parallel to the slope direction, affects the acceleration and deceleration of downslope flows and, consequently, influences erosion and deposition Plan and profile curvature maps were reclassified into three classes, namely convex, flat, and concave lands, with negative, zero, and positive values, respectively (Figs 6.2g and h) In addition to the topographical factors, land use, NDVI, and vegetation density are key conditioning factors that contribute to the occurrence of landslides Sparsely vegetated areas are more prone to erosion and increased instability than forests Vegetation strengthens the soil through an interlocking network of roots that forms erosion-resistant mats that stabilize slopes Evapotranspiration controls the wetness of slopes NDVI is frequently considered a 118 Fig 6.2 Landslide conditioning factor used in the current study B Pradhan and M.I Sameen Landslide Susceptibility Modeling … Fig 6.2 (continued) 119 120 Fig 6.2 (continued) B Pradhan and M.I Sameen Landslide Susceptibility Modeling … Fig 6.2 (continued) 121 122 controlling factor in landslide susceptibility mapping In general, when the value of NDVI is high, the area covered by vegetation is large Furthermore, a relatively low vegetation coverage can easily lead to a landslide incident In this study, a land use layer that consisted of 10 classes was used for LSM Vegetation density was reclassified into four classes, namely non-vegetation, low vegetation, moderate vegetation, and dense vegetation (Fig 6.2a) NDVI was reclassified into six classes starting from the lowest value of −0.521 to 0.96 (Fig 6.2b) Four hydrological factors were also used for LSM in the current study TWI describes the effects of topography on the location and size of saturated source areas of runoff generation This index is calculated using Ln[AS/tan(b)], where AS is the specific catchment area of each cell, and b represents the slope gradient (in degrees) of the topographic heights SPI, which is a measure of the erosion power of a stream, is also considered a factor that contributes to the stability of the study area This index is expressed as SPI = AS Â tan(b), where AS is the area of a specific catchment, and b is the local slope gradient measured in degrees STI, which reflects the erosive power of overland flow, is derived by considering transport capacity limiting sediment flux and catchment evolution erosion theories TRI is another important factor that affects landslide susceptibility These hydrological factors were reclassified into six classes using the quantile method and then applied in LSM Anthropogenic factors, such as distance to roads, distance to rivers, and distance to faults, have been considered important factors that influence landslides Extensive excavations, application of external loads, and vegetation removal are some of the most common actions that occur along road network slopes during their construction The intermittent flow regime of a hydrological network and gullies encompasses erosive and saturation processes, thereby increasing pore water pressure and leading to landslides in areas adjacent to drainage channels In addition, geological faults are important triggering factors of landslides The fracturing and shearing degree plays an important role in determining slope instability Proximity (buffers) to these structures increases the likelihood of landslides given that selective erosion and the movement of water along fault planes promote these phenomena The aforementioned layers were reclassified into six classes using the quantile method B Pradhan and M.I Sameen 6.3 Methodology 6.3.1 Overall Research Flow This study encompasses four methodological steps The first step is the multicollinearity and factor effect analyses In the second step, relevant factors among the initial 15 landslide conditioning factors are selected using ACO The third step involves the application of the susceptibility models using several experiments that aim to analyze the effects of relevant factors In the last step, susceptibility models are validated using receiver operator characteristic (ROC) curves The overall workflow of this study is shown in Fig 6.3 6.3.2 Selection of Relevant Factors Using ACO ACO is a metaheuristic optimization technique whose applications have developed significantly The advantages of ACO include a probabilistic decision in terms of artificial pheromone trails and local heuristic information These advantages enable the exploration of a larger number of solutions compared with that of greedy heuristics (Gottlieb et al 2003) The overall workflow of the ACO-based landslide factor selection is presented in Fig 6.4 First, ants were generated and then placed randomly on a graph, i.e., each ant starts with one random landslide factor The number of ants placed on the graph may be set to be equal to the number of factors of the data; each ant initiates a path construction at a different factor The ants traverse nodes probabilistically from their initial positions until a traversal stopping criterion is satisfied The resulting subsets are gathered and evaluated When an optimal subset has been found or when the algorithm has been executed a certain number of times, the process stops and the best encountered factor subset is outputted If none of these conditions hold, then the pheromone is updated, a new set of ants are created, and the process is reiterated 6.3.3 Susceptibility Models In this study, susceptibility maps were produced using two data mining approaches: SVM and RF These algorithms were used to determine whether the results were consistent or the performance of the susceptibility models with Landslide Susceptibility Modeling … 123 Fig 6.3 Overall research activities used to optimize landslide conditioning factors, conduct factor effect analysis, and develop improved susceptibility models Fig 6.4 Overall workflow of factor subset selection by ACO method 124 significant factors varied from one model to another The subsequent sections briefly describe the basic concept of the algorithms 6.3.3.1 SVM SVM was originally developed by Vladimir and Vapnik (1995) as a more recent machine learning method than artificial neural networks SVM uses the training data to convert the original input space implicitly into high-dimensional feature space based on kernel functions (Brenning 2005) Subsequently, the optimal hyperplane in the feature space is determined by maximizing the margins of class boundaries (Abe 2005) Therefore, SVM training is modeled by constraining the duality optimal solution In general, kernel types include linear, polynomial, and radial basis function (RBF) or Gaussian kernels The RBF kernel was applied in this study because it was proven to be the most powerful kernel for addressing nonlinear cases (Yao et al 2008) 6.3.3.2 RF RF is an ensemble machine learning method that generates numerous classification trees that are combined to compute a classification (Breiman et al 1984; Breiman 2001) Hansen and Salamon (1990) indicated that a necessary and sufficient condition for an ensemble of classification trees to be more accurate than any of its individual member was that the members of the ensemble must perform better than random members and should be diverse RF increases diversity among classification trees by resampling the data with replacement and randomly changing the predictive variable sets over different tree induction processes The RF algorithm involves two main user-defined parameters that require appropriate specifications: the number of trees (k) and the number of predictive variables A predictive variable may be numerical or categorical, and translation into the design variables is unnecessary An unbiased estimate of the generalization error is obtained during the construction of an RF The proportion of misclassifications (%) overall out-of-bag (OOB) elements is called the OOB error The OOB error is an unbiased estimate of the generalization error Breiman (2001) proved that RF produces a limiting value of the generalization error As the number of trees increases, the generalization error always converges The value of k must be set sufficiently high to allow this convergence The RF algorithm estimates the importance of a predictive variable by examining the OOB errors An increase in the OOB error is relative to predictive variable importance B Pradhan and M.I Sameen The advantages of RF include resistance to overtraining and the capability to grow a large number of RF trees without creating a risk of overfitting RF algorithm data not need to be rescaled, transformed, or modified; they are also resistant to outliers in predictors In this study, the number of trees in an RF was fixed at 500 for RF modeling after a primary analysis, and m sampled at each node was set at to analyze the combined contributions of subsets of features while maintaining fast convergence during iterations No calibration set is required to regulate the parameters (Micheletti et al 2014) The importance and standardized rank of each landslide variable were calculated The ranks were then used to overlay landslide factors and generate the susceptibility maps 6.4 Results 6.4.1 Multicollinearity Analysis Multicollinearity analysis is an important step in LSM The existence of a near-linear relationship among factors can create a division-by-zero problem during regression calculations This problem can cause the calculations to be aborted and the relationship to be inexact; division by an extremely small quantity still distorts the results Therefore, analyzing landslide conditioning factors before LSM is important In multicollinearity analysis, collinear (dependent) factors are identified by examining a correlation matrix constructed by calculating R2 Various quantitative methods for detecting multicollinearities, such as pairwise scatter plots, estimation of the variance inflation factor (VIF), and investigation of eigenvalues in a correlation matrix, are available In this study, multicollinearity was detected by calculating the VIF values of each landslide conditioning factor In addition, communalities similar to R2 were calculated for each factor (Costello 2009) Communality shows how well a variable is predicted by the retained factors Table 6.1 presents the estimated communalities and VIF values for each landslide conditioning factor The second column of Table 6.1 indicates that some factors, such as land use, distance to road, distance to river, slope, STI, TWI, and TRI, exhibit strong linear relationships with other factors These factors may negatively affect the regression analysis However, VIF values are quantitative measures that are typically used to conclude whether a factor has a problem In some studies, a VIF greater than two was considered problematic, whereas in other studies, a VIF greater than 10 was considered problematic (Garrosa et al 2010) To solve the Landslide Susceptibility Modeling … Table 6.1 Calculated communalities and VIF values for each landslide conditioning factor 125 Factors Communality VIF Aspect 0.053 1.14 Land use 0.566 3.15 Vegetation density 0.044 2.9 NDVI 0.069 2.93 Distance to lineament 0.001 1.25 Distance to road 0.576 3.74 Distance to river 0.626 4.15 Altitude 0.35 2.47 Slope 0.608 9.02 Profile curvature 0.015 1.11 Plan curvature 0.1 1.25 SPI 0.311 1.57 STI 0.684 2.77 TWI 0.638 2.46 TRI 0.589 39.79 multicollinearity problem, factors can be excluded from further analysis or other sampling techniques should be examined In this study, factors with VIF values greater than 10 (e.g., TRI) were removed from further analysis 6.4.2 Factor Analysis The previous section shows that multicollinearity analysis identifies landslide factors that exhibit the problem of having a strong correlation with other remaining factors To determine underlying factors that are responsible for correlations in data, factor analysis was conducted in the current study Factor analysis is an investigative method that is applied to a Fig 6.5 Graph of factors versus the corresponding eigenvalues calculated based on the correlation matrix set of observed variables; it aims to identify underlying factors from which observed variables are generated (Roscoe et al 1982) Factor analysis using the principal component extraction method was applied in this study to determine the factors that underlay the data Figure 6.5 shows the graph of the underlying factors versus the eigenvalues calculated based on the correlation matrix The graph provides information about the factors It was used to determine how well the selected number of components fit the data The graph indicated that the first eight factors accounted for the majority of the total variability in the data (given by the eigenvalues) The remaining factors accounted for a minimum amount of the variability (nearly zero) and were likely insignificant Eigenvalue 1 Factor Number 10 11 12 13 14 15 126 Table 6.2 presents the sorted unrotated factor loadings and communalities resulting from the factor analysis Communalities describe the proportion of variability of each variable that is explained by the factors When a communality is closer to 1, the variable is better explained by the factors Variance demonstrates the variability in the data explained by each factor (i.e., the variance is equal to the eigenvalue) Meanwhile, %Var shows the proportion of variability in the data explained by each factor In the factor analysis, factors were extracted from the 15 variables All the variables were well-represented by the selected factors given that the corresponding communalities were generally high For example, 0.974 or 97.4% of the variability in aspect and profile curvature was explained by the factors In addition, the selected factors explained most of the total data variation (0.881 or 88.1%, Table 6.2) Furthermore, Table 6.2 shows the variable loading on each factor For example, distance to river (−0.823), distance to road (−0.796), land use (0.795), slope (0.779), TRI (0.77), altitude (−0.656), TWI (−0.322), and NDVI (0.324) have large absolute loadings on factor This result indicates that this subset of variables can be reduced into fewer variables By contrast, STI (0.84), TWI (0.782), SPI (0.718), and plan curvature (−0.402) have large absolute loadings on factor This finding suggests that these factors can be combined and reduced into fewer theoretical factors In addition, land use, NDVI, and vegetation density have large absolute loadings on factor 3, thereby suggesting that a theoretical factor can combine these three interrelated factors Furthermore, several factors, including slope, aspect, and profile curvature, have large loadings on factor LiDAR-derived factors and distance to the road have large absolute loadings on factor SPI, distance to lineament, and both curvature layers have a few underlying factors Aspect and profile curvature have large positive loadings on factor Plan and profile curvatures have large absolute loadings on factors 5, 6, and This finding indicates that these two variables can be combined into one variable This resulting variable can be the total curvature, which has not been used in the current study 6.4.3 ACO-Based Factor Selection Table 6.3 shows the landslide conditioning factors and their corresponding codes used in the subsequent tables This section describes the six experiments conducted in this study to analyze the effects of landslide conditioning factors on LSM The six experiments were classified into two main groups The first group included all the 14 factors (Table 6.4), whereas the second group contained only the LiDAR-derived factors In the first group, the three experiments included factors, 10 factors, and the produced B Pradhan and M.I Sameen susceptibility models that used all the 14 factors In the second group, the three experiments involved LiDAR factors, LiDAR factors, and LiDAR factors, which were the total number of LiDAR factors derived from the DEM These subsets were evaluated using the SVM and RF models The selected factors and the prediction accuracy rate of both models are presented in Table 6.4 The results showed that using all the conditioning factors did not necessarily guarantee the highest accuracy In the case of the first group, the highest accuracy was achieved with either 10 or 14 factors when the RF model was used In the case of the SVM model, using all the 14 factors produced the highest accuracy In the three experiments in the first group, the RF model performed better than the SVM model However, no significant difference was found between using all the 14 factors and using only 10 factors in the susceptibility analysis for both the SVM and RF models In the experiments in the second group, accuracy decreased by approximately 0.16 on average This result indicated that some factors, such as land use, vegetation density, and NDVI, were important for predicting landslides in the study area The highest accuracy was achieved using the RF model with LiDAR factors The RF model with only factors selected via ACO performed better than the SVM model with LiDAR factors In the SVM model, the findings indicated that using only LiDAR factors yielded better results than using factors mainly because the selected individual factors in the subset with factors were more important than those selected in the subset with factors Consequently, including additional factors to LiDAR-derived factors was necessary for accurate LSM in the study area The RF model performed better than the SVM model even with fewer factors The second subset of the first group, which had 10 factors that included LiDAR-derived and non-LiDAR-derived factors, was recommended to produce landslide susceptibility maps in the study area for land use planning 6.4.4 Landslide Susceptibility Models In the current study, four landslide susceptibility maps were produced for the study area (Fig 6.6) These maps were generated using the SVM and RF models with the best subsets of the two groups as described in the previous section The first examination of the maps showed no spatial agreement among the susceptibility classes of the four models For example, the maps produced using a combination of LiDAR and non-LiDAR factors were different from those produced using only LiDAR factors In addition, the two maps produced using the SVM and RF models with the significant factors selected among the 14 factors were different The apparent difference was mainly observed in the middle part of the study area The map produced using the 0.216 0.003 −0.157 4.0649 0.271 Profile Curvature Variance % Var 0.138 2.0748 −0.402 −0.213 Plan Curvature 0.118 1.7765 −0.112 −0.09 0.112 −0.119 0.274 Aspect 0.088 1.3129 −0.442 −0.182 0.404 0.691 0.086 1.2884 0.448 0.503 −0.153 −0.002 0.173 0.14 −0.009 −0.835 0.164 0.109 −0.005 0.324 −0.05 NDVI Distance to Lineament 0.718 0.278 0.265 Vegetation Density 0.121 −0.112 0.033 −0.196 −0.022 0.104 SPI 0.048 0.015 −0.851 0.782 −0.322 TWI −0.427 −0.434 −0.413 0.105 −0.252 −0.345 Factor5 0.188 −0.295 0.051 0.84 0.236 STI −0.125 −0.303 0.182 −0.176 −0.04 Factor4 0.102 −0.045 −0.656 −0.174 −0.138 0.77 Altitude −0.175 TRI 0.304 −0.14 Slope −0.003 0.795 0.779 Land use −0.289 −0.04 −0.796 Distance to Road −0.194 −0.025 −0.823 Distance to river Factor3 Factor2 Factor1 Variable Table 6.2 Sorted unrotated variable loadings on extracted factors resulted from factor effect analysis 0.072 1.0738 0.424 0.374 −0.073 0.586 −0.043 −0.061 0.403 −0.175 0.092 0.29 0.216 0.21 0.074 −0.019 0.131 Factor6 0.06 0.8964 0.369 0.056 0.817 −0.124 −0.033 −0.002 −0.114 0.13 0.134 −0.042 0.025 0.016 −0.025 0.083 0.12 Factor7 0.049 0.7292 −0.474 0.534 0.11 0.084 −0.07 −0.005 0.037 0.089 0.189 −0.257 0.084 0.081 −0.206 0.166 −0.021 Factor8 0.881 13.217 0.974 0.93 0.974 0.872 0.899 0.905 0.707 0.789 0.875 0.818 0.971 0.97 0.817 0.848 0.868 Communality Landslide Susceptibility Modeling … 127 128 B Pradhan and M.I Sameen Table 6.3 Assigned code of each landslide conditioning factor Factor Code Aspect Distance to Road Land use Distance to river SPI 12 Vegetation density Altitude STI 13 NDVI Slope TWI 14 Distance to lineament Profile curvature 10 RF model exhibited nearly moderate and very high susceptibility in the middle part of the study area, whereas the map produced using the SVM model exhibited high and very high susceptibility in the same area The southeastern part of the study area had very low and low susceptibility based on the RF model, whereas its susceptibility was moderate and high based on the SVM model Consequently, no exact spatial agreement was found on the susceptibility classes in most parts of the study area based on the two models The susceptibility maps produced using only LiDAR-derived factors are different from those produced using the significant factors selected among the 14 factors However, spatial agreements were found among the susceptible zones in the northern, middle, and southern parts of the study area when the RF- and SVM-generated maps were compared 6.4.5 Validation The ROC curve is a graph with a false positive rate plotted on the x-axis and a true positive rate plotted on the y-axis It uses a visual comparison of the performance of the methods The area under the ROC curve (AUC) shows the global accuracy statistics for each model If the AUC (which varies from 0.5 to 1) increases, then the prediction performance of the method increases (Erener and Düzgün 2010) Figure 6.7 shows the plotted ROC curves and the estimated AUC values for the four susceptibility maps described in previous section On the one hand, the highest accuracy was achieved using the RF model with 10 factors selected among the 14 initial factors On the other hand, the lowest accuracy was achieved using the SVM model with only LiDAR-derived factors 6.5 Discussion and Conclusion In this study, we optimized landslide conditioning factors and conducted a factor effect analysis to provide useful information about landslide susceptibility analysis in the Cameron Highlands, Malaysia This study first identified problematic factors by calculating VIF values during multicollinearity analysis As mentioned earlier, problematic factors can disrupt or distort the regression results Plan curvature 11 Therefore, removing these factors is an essential step in LSM The communality of each variable was calculated from the correlation matrix The communalities indicated that land use (0.566), distance to road (0.576), distance to river (0.626), altitude (0.35), slope (0.608), SPI (0.311), STI (0.684), TWI (0.638), and TRI (0.589) demonstrated relatively strong correlations with other factors However, only TRI was problematic (given by the VIF) based on the selected threshold (VIF > 10 was considered problematic), and thus, it was excluded from LSM In addition, slope had a relatively high VIF of approximately 10 However, slope is the most important factor for LSM, and thus, it has been retained In future studies, this problem could be solved by using different sampling procedures, such as landslide polygons instead of the centroid of landslides, which was adopted in the current study The use of different sampling procedures or the removal of inaccurate landslide inventories may solve the problem of collinear factors Factor analysis was conducted to identify underlying factors The eigenvalues showed that the first factors accounted for the majority of the total variability in the data The remaining factors accounted for a minimal amount of the variability (approximately 0) and were likely insignificant Therefore, factors were extracted from the 15 landslide conditioning factors The corresponding communalities were generally high, and thus, the landslide-related variables were well-represented by the factors The highest percentage of over 97% of the variability in aspect and profile curvature was explained through these extracted factors In general, the factor effect analysis suggested reducing the number of landslide conditioning factors by combining some of the factors into fewer theoretical factors For example, plan and profile curvature were highly recommended to be combined (Table 6.2) To achieve such combination, a comprehensive analysis of landslide conditioning factors is required In addition, distance to river (−0.823), distance to road (−0.796), land use (0.795), slope (0.779), TRI (0.77), altitude (−0.656), TWI (−0.322), and NDVI (0.324) were found to have large absolute loadings on factor This result indicated that this subset of variables could be reduced into fewer theoretical factors Thereafter, ACO was used to select significant variable subsets from the available variables The SVM and RF classification models were adopted to evaluate the selected Landslide Susceptibility Modeling … 129 Fig 6.6 Landslide susceptibility maps subsets A total of six experiments were conducted in the study to analyze the effects of landslide conditioning factors on LSM These experiments were as follows: factors, 10 factors, all the 14 factors, LiDAR factors, LiDAR factors, and LiDAR factors The evaluation of the six experiments showed that the RF model with 10 landslide factors selected from among the 14 factors produced the best result (AUC = 0.95) In addition, a significant decrease in 130 B Pradhan and M.I Sameen Fig 6.7 ROC curves of the produced susceptibility map Fig 6.8 Percentages of landslide inventories in each susceptibility zone Table 6.4 Results of factor subset selection of ACO-based experiments Dataset All data Only LiDAR Experiment accuracy was observed when only the LiDAR-derived factors were used Factors, such as land use, vegetation density, and NDVI were found to be important for predicting landslides in the study area In this study, landslide susceptibility maps were produced for the study area The susceptibility maps produced using only LiDAR-derived factors were different from those produced using significant factors selected from all the 14 factors This study showed that spatial agreement on susceptibility zones decreased by adding non-LiDAR factors in the analysis A visual interpretation of the susceptibility maps indicated spatial agreements on susceptible zones in the northern, middle, and southern parts of the study area when LiDAR-based factors were used Therefore, statistical validation methods, such as ROC curves and spatial agreement analysis should be considered to decide whether a map can be used for land use planning In addition, Fig 6.8 shows the percentages of landslides in each susceptibility class The graph shows that most of the landslides are located in high and very high susceptibility zones In general, the RF model performed better than the SVM algorithm regardless of the combination of factors used for modeling Although the parameters of the SVM algorithm were fine-tuned in the current study, concluding that RF should be used for LSM in the Cameron Highlands would be difficult This study suggests that significant attention should be directed toward analyzing input landslide factors Moreover, problematic factors and observations should be removed Several factors are typically derived from a LiDAR DEM, and thus, collinearity can be found among these factors Therefore, additional factors, including non-LiDAR factors, should always be used in LSM Sometimes, factors such as distance to the road have a strong correlation with land use The careful design of classification schemes when producing land use maps is recommended Total number of factors Selected factors AUC SVM RF 5-Factors 14 [7 9] 0.83 0.89 10-Factors 14 [2 10 12 14 7] 0.89 0.95 14-Factors 14 [9 10 12 11 14 13] 0.91 0.95 3-Factors [3 2] 0.72 0.77 6-Factors [6 7] 0.69 0.70 8-Factors [4 1] 0.75 0.81 Landslide Susceptibility Modeling … For example, roads can be classified into different classes based on road type or width Such classification can reduce the correlation among landslide factors, and thus improve LSM This study examined the optimization of landslide conditioning factors and conducted a factor effect analysis to improve understanding of susceptibility models However, several issues should be considered in future studies First, the effects of landslide sampling procedures and the spatial resolution of DEMs should be investigated in detail Attention should also be directed toward developing new theoretical factors in future studies LiDAR-derived factors can be reduced into fewer factors, which can decrease collinearity among factors Quantitative accuracy indicators, such as AUC, may be insufficient when deciding which algorithm or LSM approach should be used Therefore, new indicators that consider spatial agreements on susceptible classes should be developed In summary, comprehensive analysis on landslide conditioning factors should be conducted to improve understanding of 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investigate the optimization of landslide conditioning factors and conduct a factor effect analysis. .. among landslide factors, and thus improve LSM This study examined the optimization of landslide conditioning factors and conducted a factor effect analysis to improve understanding of susceptibility. .. study, we optimized landslide conditioning factors and conducted a factor effect analysis to provide useful information about landslide susceptibility analysis in the Cameron Highlands, Malaysia This