Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm: A case study of Baihetan Reservoir Area - Guo, Junqi; Xi, Wenfei; Yang, Zhiquan; Shi, Zhengtao; Huang, Guangcai; Yang, Zhengrong; Yang, Dongqing (Journal of mountain science, 2024)
Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction. In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models, in this study, a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and SSA-BP (Sparrow Search Algorithm-Back Propagation) neural network algorithm. The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area, update the cataloging data of landslide hazards, and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation. Baihetan Reservoir area is selected as a case study for validation. As indicated by the results, the application of SBAS-InSAR technology, combined with both ascending and descending orbit data, effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data (e.g., shadow, overlay, and perspective contraction) in deep canyon areas, thereby enabling the acquisition of up-to-date landslide hazard data. Moreover, in comparison to the conventional BP (Back Propagation) algorithm, the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase, with mean squared error and mean absolute error reduced by 0.0142 and 0.0607, respectively. Additionally, during the process of susceptibility evaluation, the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors. The area under the curve of this model reaches 0.909, surpassing BP (0.835), random forest (0.792), and the information value method (0.699). The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope, surface temperature, and deformation rate, while it is negatively correlated with fault distance and normalized difference vegetation index. Geological lithology exerts minimal influence on the occurrence of landslides, with the risk being low in forest land and high in grassland. The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.