Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

Abstract

Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.

Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

“…Statistical models are most commonly used in landslide susceptibility mapping, which are based on the analysis of the relationships between influencing factors and existing landslides [7]. In these statistical approaches, bivariate and multivariate statistical techniques are used for landslide susceptibility mapping throughout the world, including frequency ratio [8][9][10], index of entropy [11][12][13][14][15], bivariate statistical analysis [16], multivariate adaptive regression spline [17], analytical hierarchy process [18,19], statistical index [20,21], weight of evidence [13,21], evidential belief function [22,23], certainty factor [24,25], and logistic regression [26][27][28][29].…”

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

“…Geographic Information System (GIS) has been used as an effective tool for spatial analysis and data manipulation due to its ability to handle large amounts of spatial data [11]. Specifically, the combination of statistical and probabilistic models with Remote Sensing (RS) and GIS has been widely used by different researchers [1,12]. Additionally, some scientists and researchers have studied natural disasters, specifically floods and FSM, with the help of RS and GIS, using different models such as Decision-Tree (DT) [6,13], Support Vector Machine (SVM) [14,15], Frequency Ratio (FR) [16,17], Evidential Belief Function (EBF) [18][19][20], EBF-AHP (Analytical Hierarchy Process) [21], Logistic Regression (LR) [22], Shannon's entropy and weights-of-evidence [23], Artificial Neural Networks (ANN) [23], AHP [23,24], Random Forest [3,23], and Adaptive Neuro-Fuzzy Inference System (ANFIS) [25,26].…”

“…Their finding show that although MCDM models could predict flood-prone areas, the data mining algorithms had a higher prediction power than MCDMs since MCDMs rely on expert opinion. Arabameri et al [28] applied an EBF model to the generation of flood susceptibility maps and compared the results with FR, TOPSIS, and VIKOR models, concluding that the EBF model performed best.Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”

“…Some research shows that bivariate statistical models demonstrated better predictive power than both machine learning and data mining algorithms [23]; this is because machine learning and data mining algorithms are more complex and require an expert to perform accurate simulations. Therefore, bivariate models, which are very simple to run with similar or sometimes superior predictive power, can be used as adequate substitutes.At present, the EBF method is rarely applied for flood analysis, though it has been used for other categories of natural disasters such as landslide susceptibility assessment [21,33,58], and land subsidence [12,59], as well as having been used to predict groundwater potential zones [19,60].The main purpose of this research is to evaluate the possibilities of using the EBF method to generate flood susceptibility maps and to assess the strengths and weaknesses of this method, as EBF has rarely been used for floods but has shown high accuracy in previous studies involving natural hazards mapping. The specific objectives of the current study include, (i) determining the most significant factors in each model, (ii) application of a bivariate statistical model, EBF, to produce new ensemble models, in combination with a statistical model, LR, in order to generate a more accurate flood susceptibility map, (iii) detection of the flood-prone areas within the study region for improved management during flood occurrence.…”

“…Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”