ZHANG M M, LI S D, WANG X, et al.Landslide susceptibility assessment based on machine learning and encoder coupling[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(2):69-80.

doi:10.16186/j.cnki.1673-9787.2023120035

Received:2023/12/12

Revised:2024/04/09

Published:2025-03-05

Landslide susceptibility assessment based on machine learning and encoder coupling

ZHANG Mengmeng1, LI Shaoda1, WANG Xiao2, LI Xinyue3, DAI Keren1,4

1.College of Earth and Planetary Science， Chengdu University of Technology， Chengdu  610059， Sichuan， China；2.School of Architecture and Civil Engineering， Chengdu University， Chengdu  610106， Sichuan， China；3.Mahindra United World College of India， Pune， MH  412108， India；4.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection， Chengdu University of Technology， Chengdu  610059， Sichuan， China

Abstract: Objectives To enhance the ability of machine learning models to extract data features with limited samples and improve the predictive accuracy of the models，   Methods Jiulong 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.   Results The 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.  Conclusions The 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. 

Key words:Landslides;susceptibility modelling;LR;SVM;encoders