Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning—A Case Study of Mao County, Sichuan Province - Su, Yulin; Rong, Guangzhi; Ma, Yining; Chi, Junwen; Liu, Xingpeng; Zhang, Jiquan; Li, Tiantao (Frontiers in Earth Science, 2022)
Geological disasters are one of the major natural disasters in China, while earthquakes are one of the major triggers for them in mountain area. In this study, a Deep Neural Network (DNN) model suitable for assessing hazard degree of the earthquake-geological disaster chain was established, and the performance of support vector machines, logistic regression, and random forest models were compared and discussed. This study firstly selected 9 key factors that have an impact on the geological disaster to establish a multi-source data set. Secondly, the DNN model was used to calculate the landslide susceptibility value, and verify it with the ROC curve. The evaluation was based on the landslide inventory of this county containing 427 hazard points, and the terrain, geological and seismic factors from data set available. The verification results show that the landslide sensitivity map generated by the AUC map, the DNN model has the highest accuracy (97.5%), followed by the LR model (96.7%), the SVM model (96.4%) and the RF model (95.4%). Judging from the verification results of the AUC curve, the map generated by the model has good properties, which proves the model is a promising tool for assessing hazard of earthquake disaster chains in geological disaster management.