Complex cross-regional landslide susceptibility mapping by multi-source domain transfer learning

Abstract

Landslide susceptibility evaluation plays an important role in disaster prevention and reduction. Feature-based transfer learning (TL) is an effective method for solving landslide susceptibility mapping (LSM) in target regions with no available samples. However, as the study area expands, the distribution of landslide types and triggering mechanisms becomes more diverse, leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift. To address this, this study proposes a Multi-source Domain Adaptation Convolutional Neural Network (MDACNN), which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas. The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models (TCA-based models). The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms, thereby significantly reducing prediction bias inherent to single-source domain TL models, achieving an average improvement of 16.58% across all metrics. Moreover, the landslide susceptibility maps generated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area, providing a powerful scientific and technological tool for landslide disaster management and prevention.

Keywords

Landslide susceptibility / Deep learning / MDACNN / Feature domain adaptation / Data scarcity

CRediT authorship contribution statement

Yan Su: Writing - review & editing, Writing - original draft, Visualization, Validation, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Con-ceptualization. Jiayuan Fu: Writing - original draft, Software, Methodology, Investigation, Formal analysis, Data curation. Xiaohe Lai: Writing - review & editing, Writing - original draft, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualiza-tion. Chuan Lin: Writing - review & editing, Methodology, Funding acquisition, Formal analysis, Conceptualization. Lvyun Zhu: Writ-ing - review & editing, Investigation, Funding acquisition, Concep-tualization. Xiudong Xie: Writing - review & editing, Resources, Investigation. Jun Jiang: Writing - review & editing, Resources, Investigation, Data curation. Yaoxin Chen: Writing - review & editing, Software, Methodology, Conceptualization. Jingyu Huang: Writing - review & editing, Methodology, Formal analysis. Wen-hong Huang: Writing - review & editing, Methodology, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing finan-cial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The author acknowledges the technical and financial support provided by the National Natural Science Foundation of China (Grant No. 42301002, and 52109118), Fujian Provincial Water Resources Science and Technology Project (Grant No. MSK202524), and Guidance fund for Science and Technology Pro-gram, Fujian province (Grant No. 2024Y0002).

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