Landslides are destructive geological hazards that occur all over the world. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA. The Wushan segment of TGRA was selected as a case study. At first, 165 landslides were identified and a total of 14 landslide causal factors were constructed from different data sources. Multicollinearity analysis and information gain ratio (IGR) model were applied to select landslide causal factors. Subsequently, the landslide susceptibility mapping using the calculated results of four models, namely, support vector machines (SVM), artificial neural networks (ANN), classification and regression tree (CART), and logistic regression (LR). The accuracy of these four maps were evaluated using the receive operating characteristic (ROC) and the accuracy statistic. Results revealed that eliminating the inconsequential factors can perhaps improve the accuracy of landslide susceptibility modelling, and the SVM model had the best performance in this study, providing strong technical support for landslide susceptibility modelling in TGRA.

Landslide susceptibility mapping (LSM) can provide valuable information for local governments in landslide prevention and mitigation. Despite significant improvements in the predictive performance of LSM, it remains a challenge to be carried out in areas with limited availability of data. For example, in the early stage of road construction, landslide inventory data can be particularly scarce, while there is a high need to have a susceptibility map. This study aims to set up a novel procedure for coupling the knowledge-driven and data-driven models for LSM in an area with limited landslide inventory data. In particular, we propose a two-step approach. The first step consists of applying four data-driven models (logistic regression, decision tree, support vector machines, and random forest (RF)) to derive a regional susceptibility map. In the second step, the application of a heuristic model (analytic hierarchy process, AHP) is proposed to calculate a local susceptibility map for the areas with incomplete landslide inventories. The final landslide susceptibility map is obtained by merging the most accurate regional map (RF) with the local map. We apply this novel procedure to a landslide-prone region with developed road construction (National Highway G69) in Wanzhou district, where landslide inventory is difficult to update due to timely recovery from landslide-induced road damage. Results show that the proposed methodology allows identifying new landslide-prone areas, and improving LSM predictive performance, as demonstrated by the fact that two new landslides developed along G69 were perfectly classified in the highly susceptible areas. The results show that implementing the landslide susceptibility assessment with different geographical settings and combining them into best-sensitivity partitions is more accurate than focusing on creating new models or hybrid models.

The landslide development laws vary in different landslide-prone areas, hence the susceptibility models often perform in varied ways in different regions. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). These landslides seriously threaten the safety of local residents and their property. It is crucial to find the model that can generate a landslide susceptibility map with higher accuracy in the TGRA. The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA.The Wushan segment of TGRA was selected as a case study, which is located in the middle reaches of the TGRA, the southwest of China. In this study, 165 landslides were identified and 14 landslide causal factors were constructed from different data sources at first, including altitude, slope, aspect, curvature, plan curvature, profile curvature, stream power index, topographic wetness index (TWI), terrain roughness index, lithology, bedding structure, distance to faults, distance to rivers, and distance to gully. Subsequently, multicollinearity analysis and information gain ratio model were applied to select landslide causal factors. After removing five factors (altitude, TWI, profile curvature, plan curvature, curvature), the landslide susceptibility mapping using the calculated results of four models, which were support vector machines (SVM), artificial neural networks, classification and regression tree, and logistic regression. Finally, the accuracy of the four models was evaluated and compared using the accuracy statistic methods and the receiver operating characteristic (ROC). The results of accuracy analysis showed that the SVM model performed the best. At the same time, the SVM performance behavior for susceptibility modelling in other areas were collected. In these regions, the accuracy of SVM was always larger than 0.8. We could see that SVM performed acceptably in different regions, and thus it can be used as a recommended model in TGRA and other landslide-prone regions.In this study area, a total of 62% of the landslides were within 300 m from the Yangtze River, and the distance to rivers was the most important factor. The impoundment of the TGRA impacted the landslide development in three aspects: (1) the long-term immersion of reservoir water gradually reducing the strength of rock (soil) at the saturated zone (mostly near the Yangtze river), reducing the resistance force of landslide; (2) the strong dynamic action of water enhancing the lateral erosion on the bank slope, changing the slope shape, and thus reducing the slope stability; (3) the periodic fluctuation of the reservoir water making the self-weight, static, and dynamic water pressure of the landslide change, which could increase the resistance force or reduce the sliding force of the landslide and even cause overall instability and damage. Hence, in order to reduce the losses caused by landslides in TGRA, we should pay more attention to the early warning of reservoir bank landslides.