The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility.

A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan

Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques

Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.

This paper presents GIS-based new ensemble data mining techniques that involve an adaptive neuro-fuzzy inference system (ANGIS) with genetic algorithm, differential evolution, and particle swarm optimization for landslide spatial modelling This research was tested in Hanyuan County, which is a landslide-prone area in Sichuan Province, China Different continuous and categorical landslide conditioning factors according to a literature review and data availability were selected, and their maps were digitized in a GIS environment These layers are the slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index, distance to faults, distance to rivers, distance to roads, lithology, normalized difference vegetation index and land use According to historical information of individual landslide events, interpretation of the aerial photographs, and field surveys supported by the Sichuan Land Resources Bureau of China, 225 landslides were identified in the study area The landslide locations were divided into two subsets, namely, training and validating (70/30), based on a random selection scheme In this research, a probability certainty factor (PCF) model was used for the evaluation of the relationship between the landslides and conditioning factors In the next step, three data mining techniques combined with the ANFIS model, including ANFIS-genetic algorithm (ANFIS-GA), ANFIS-differential evolution (ANFIS-DE), and ANFIS-particle swarm optimization (ANFIS-PSO), were used for the landslide spatial modelling and its zonation Finally, the landslide susceptibility maps produced by the mentioned models were evaluated by the ROC curve The results showed that the area under the curve (AUC) of all of the models was > 075 At the same time, the highest AUC value was for the ANFIS-DE model (0844), followed by ANGIS-GA (0821), and ANFIS-PSO (0780) In general, the proposed ensemble data mining techniques can be applied for land use planning and management of landslide susceptibility and hazard in the study area and in other areas

Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling

The main purpose of this study was to map landslide susceptibility through the AHP and CF models, using a geographic information system (GIS), for the Baozhong region of Baoji City, China. At first, a landslide inventory map was prepared using technical reports, aerial photographs, and coupling with field surveys. A total of 79 landslides were mapped, out of which 55 (70 %) were randomly selected for building landslide susceptibility models, while the rest 24 landslides (30 %) were applied for validating the models. 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 maps were produced using the AHP and CF models. Finally, the validation of landslide susceptibility map was accomplished with areas under the curve (AUC) and the Seed Cell Area Index (SCAI). The AUC plot estimation results indicated that the susceptibility map applying CF model has a higher prediction accuracy of 81.43 % than the accuracy of 75.97 % applying AHP model. Similarly, the validation results also showed that the success rate of the CF model was 85.93 %, while the success rate was 77.80 % for the AHP model. According to the validation results of the AUC evaluation, the map produced by CF model behaves better performance. Furthermore, the validation results using the SCAI also indicated that the CF model has a higher predication accuracy than the AHP model. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation.

GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China

The aim of this study is to generate reliable susceptibility maps using frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) models based on geographic information system (GIS) for the Qianyang County of Baoji City, China. At first, landslide locations were identified by earlier reports, aerial photographs, and field surveys, and a total of 81 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70 % (56 landslides) for training the models, and the remaining 30 % (25 landslides) was used for validation purpose. In this case study, 13 landslide-conditioning factors were exploited to detect the most susceptible areas. These factors are slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, distance to faults, distance to rivers, distance to roads, Sediment Transport Index (STI), Stream Power Index (SPI), Topographic Wetness Index (TWI), and lithology. Subsequently, landslide-susceptible areas were mapped using the FR, SI, and WoE models based on landslide-conditioning factors. Finally, the accuracy of the landslide susceptibility maps produced from the three models was verified by using areas under the curve (AUC). The AUC plot estimation results showed that the susceptibility map using FR model has the highest training accuracy of 83.62 %, followed by the SI model (83.45 %), and the WoE model (82.51 %). Similarly, the AUC plot showed that the prediction accuracy of the three models was 79.40 % for FR model, 79.35 % for SI model, and 78.53 % for WoE model, respectively. According to the validation results of the AUC evaluation, the map produced by FR model exhibits the most satisfactory properties.

A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping

The main purpose of this study is to produce reliable susceptibility maps using GIS-based support vector machine (SVM) models and compare their performances for the Qianyang County of Baoji City, Shaanxi Province, China. In this paper, with kernel classifiers of linear, polynomial, radial basis function and sigmoid, the four various types were applied in landslide susceptibility mapping. The important input parameters for the landslide susceptibility assessment were acquired from different sources. Firstly, 81 landslide sites were obtained by aerial photographs, earlier reports and field surveys. Then, the landslide inventory was randomly classified into two datasets: 70 % (56 landslides) for training the models and 30 % (25 landslides) for validation purpose. Secondly, 15 landslide conditioning factors were selected (i.e., slope angle, slope aspect, altitude, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, NDVI, STI, SPI, TWI, geomorphology, rainfall, and lithology). Subsequently, with four types of kernel function classifiers based on landslide conditioning factors, landslide susceptibility parameters were obtained using SVM models. Finally, the rationality of landslide susceptibility maps was verified using the receiver operating characteristics with both success rate curve and prediction rate curve. The validation results showed that success rates for the four SVM models were 83.15 % (RBF-SVM), 82.72 % (PL-SVM), 81.77 % (LN-SVM), and 79.99 % (SIG-SVM). The prediction rates for the four SVM models were 77.98 % (RBF-SVM), 77.50 % (PL-SVM), 77.07 % (LN-SVM), and 76.08 % (SIG-SVM), respectively. The results showed that the RBF-SVM model had the highest overall performance.

Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China

The main goal of this study was to produce landslide susceptibility mapping by frequency ratio (FR), statistical index (SI), and index of entropy (IOE) models based on geographic information system (GIS) for the Baozhong region of Baoji, China. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out a field survey. A total of 79 landslides were mapped and out of which, 55 (70 %) were randomly selected for building landslide susceptibility models, while the remaining 24 (30 %) were used for validating the models. 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 maps were produced using FR, SI, and IOE models. 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 FR model has the highest prediction accuracy of 82.49 %, followed by the SI model (81.43 %) and the IOE model (79.62 %). Similarly, the AUC plot showed that the success rate of the three models was 84.95 % for FR model, 82.37 % for SI model, and 82.05 % for IOE model, respectively. According to the validation results of the AUC evaluation, the map produced by the FR model exhibits the most satisfactory properties. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China

Huichan Chai

Papers

GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China

A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping

Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China

Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China