Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.

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It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation.

How to reduce landslide risk economically and effectively is a very meaningful and challenging research topic. In particular, it is difficult and expensive to completely control deep-seated colluvial landslides. Taking the Kangjiapo landslide in Wanzhou district, Chongqing city, China as a case, this study focuses on measures to prevent and control the risks of deep-seated colluvial landslides through detailed investigation and monitoring. The Kangjiapo landslide is located in the Three Gorges Reservoir area, and it is part of a famous ancient landslide named the Pipaping landslide. The steep sliding surface in the rear was not found during the first treatment, the Kangjiapo landslide has been reactivated since 2015. Field investigations, monitoring, borehole and related test were conduct to identify the landslide characteristics and mechanisms. The landslide deformation was not spatially or temporally uniform according to monitoring data analysis. The landslide is less likely to fail in general because the sliding surface in the front is very gentle. The reasons for Kangjiapo landslide reactivation could include the decline of the reservoir water level, a steep sliding surface in the rear, the existence of a sliding zone with low strength due to a long period of reservoir immersion. Landslide risk mitigation measures are proposed for the deep-seated landslide, including stabilizing piles nearby the road, and a BeiDou Navigation Satellite System and MEMS inclinometers in the platform.