Landslide susceptibility assessment based on machine learning and encoder coupling

ObjectivesTo enhance the ability of machine learning models to extract data features with limited samples and improve the predictive accuracy of the models，MethodsJiulong County， Kangding City， Luding County and Muli County， the key provincial erosion prevention areas in the middle and lower reaches of the Yalong and Dadu Rivers in Sichuan Province， were selected as the study area for landslide susceptibility evaluation. Twelve influencing factors were selected to construct the landslide susceptibility evaluation index system， the coefficient of determination （CF） was used to quantify the evaluation index， and noise-reducing auto-encoders （DAEs） and convolutional auto-encoders （CAE） were added to the best-performing model by comparing the logistic regression （LR） and the support vector machine（SVM） models.ResultsThe results showed that compared with the CF-LR model， the CF-SVM model， the precision （P）， F-measure， Kappa coefficient， overall accuracy （OA）， and AUC of the CF-SVM model increased by 31.9%， 1.1%， 17.1%， 8.5%， and 8.6%， respectively， After adding the DAE encoder， the recall （R）， F-measure， Kappa coefficient， and overall accuracy （OA） of the CF-SVM-DAE model increased by 8.1%， 5.8%， 8.1%， and 4%， respectively， compared to the CF-SVM model After adding CAE encoders， the recall （R）， F-measure， Kappa coefficient， and overall accuracy （OA） of the CF-SVM-CAE model increased by 0.4%， 0.2%， 0.2%， and 0.1%， respectively， compared to the CF-SVM model.ConclusionsThe CF-SVM model has higher prediction accuracy among the selected machine learning methods. Adding DAE encoder toto the CF-SVM has better robustness than adding CAE encoder， thus the CF-SVM-DAE model performs the best among all models and is more suitable for the current study area.