Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method information value analysis in order to optimize selection negative used for machine learning. Recurrent neural network (RNN) has memory function, so when using an RNN mapping purposes, input landslide-influencing factors affects resulting quality model. The calculates factors, determines data based on importance any specific factor determining susceptibility, and improves prediction potential recurrent networks. simple unit (SRU), newly proposed variant network, is characterized by possessing faster processing speed currently less application history networks optimized Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: model sample selection, SRU model, results show that best performance terms AUC (0.9280), followed (0.9057), (0.7277), (0.6355). In addition, several objective measures accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), receiver operating characteristic also performs best. Therefore, can be sensitivity improve model’s performance; second, weaker than performance.