A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

Abstract

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China)

“…Wanzhou is a district of Chongqing (China), bordering the northwest of Sichuan Province and the southeast of Hubei Province. It is also one of the major port cities in the Yangtze River Basin and an important industrial, cultural, trade, and transportation center in Yudong area [39]. Wanzhou District lies between latitudes 30 • (Figure 1) belongs to the bank section of Wanzhou District with an area of 552 km 2 , is distributed along the Yangtze River, and contains 202 historical landslides.…”

“…There are also Paleozoic Permian strata in the local area from 285-230 million years ago, as well as the Cenozoic Quaternary strata of 2.5 million years ago. Figure 2 shows the distribution of the strata in the study area, with a scale of 1:50,000 [39].…”

“…Wanzhou District belongs to the subtropical monsoon humid zone with a mild climate and abundant rainfall. The annual average precipitation is 1191.3 mm, and almost 70% of the annual precipitation falls from May-September [39].…”

Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China

“…In this work, the Yangtze River was excluded from the study area because the values of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) data always change dramatically at the junction between this river and its sides [67]. It is known that landslide occurrences are greatly relevant to causative factors.…”

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

“…In recent years, machine learning methods have been widely used in landslide susceptibility modeling and have achieved remarkable results because of their high prediction accuracy and absence of a prior knowledge requirement. As such, this approach produces a higher prediction accuracy, can more precisely identify the nonlinear relationship between input and output variables, and retains more characteristic information from the original data [21][22][23][24]. This approach includes multiple adaptive regression splines [25,26], fuzzy logic [27,28], artificial neural network [15,29], multilayer perceptron [30], decision tree [31][32][33], random forest [34][35][36], support vector machine [37][38][39], rule-based approach [40], and multi-criteria evaluation techniques [41], among others.…”