Cross-regional extrapolation of landslide susceptibility mapping via transfer learning

Abstract

Landslide susceptibility mapping (LSM) is an essential tool for the prevention and management of landslide-related disasters. Conventional machine learning-based LSM method faces significant limitations in cross-regional extrapolation. To address this challenge, this study develops a transfer learning (TL) model based on the Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) framework, specifically designed for cross-regional LSM. A total of 11 modelling scenarios is established to compare the cross-regional extrapolation performance of Random Forest (RF), CNN-BiLSTM, and TL models, with Wanzhou District and Wushan County in Chongqing used as case studies. The results indicate that, compared to the strategy of directly expanding training dataset used by RF and CNN-BiLSTM models, the pre-training and fine-tuning strategy employed by the TL model is more suitable for county-scale LSM and its cross-regional extrapolation. Additionally, the cross-regional extrapolation performance of the TL model improves as the volume of source domain data increases. Finally, the SHAP algorithm is used to provide a global interpretation of the TL #3 model, which demonstrates the best performance in cross-regional model extrapolation.

Keywords

Landslide susceptibility mapping / Transfer learning / Cross-regional extrapolation / SHAP

CRediT authorship contribution statement

Yunhao Wang: Writing - original draft, Methodology, Conceptualization. Wengang Zhang: Writing - review & editing, Supervision. Luqi Wang: Validation. Songlin Liu: Software. Kaiqiang Zhang: Data curation. Pengfei Liu: Data curation. Weixin Sun: Validation. Shuihua Jiang: Validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The corresponding author Wengang Zhang is Associate Editor of this Journal, and was not involved in the editorial review or the decision to publish this article.

Acknowledgements

This work was supported by the National Key R&D Program of China (2024YFC3211204), Open Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP2024K021), China Postdoctoral Foundation (2024M753842), Science and Technology Research Program of Chongqing Municipal Education Commission (HZ2021001), Science and Technology Project from Chongqing Baima Shipping Development Co., Ltd (GS-HFBM-Z-2025-0002-JS-0001), and Sichuan Transportation Science and Technology Project (2018-ZL-01). The data were obtained from Resource and Environment Science and Data Center and Chongqing Geological Monitoring Station.

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