From prediction to regionalization: Enhancing flash flood susceptibility mapping using machine learning and GeoDetector

Abstract

Flash floods cause substantial economic losses and casualties worldwide. Susceptibility-based flash flood mapping supports the development of effective flood mitigation strategies. While machine learning (ML) models offer superior accuracy, converting their outputs into spatially coherent and actionable maps remains challenging. Existing susceptibility maps often rely on subjective discretization and exhibit fragmented spatial patterns, limiting their utility in practice. In this context, this study proposes a novel framework that achieves the effective transformation of susceptibility prediction results into a management-oriented regionalization map. The framework integrates supervised learning, unsupervised clustering, and spatial explanatory feedback to enable information fusion and spatial restructuring of multi-model outputs. Flash flood susceptibility was first modelled using two supervised algorithms: Random Forest and CatBoost. Their outputs, along with exposed elements, were integrated and discretized using a two-stage clustering approach based on Self-Organizing Maps (SOM) and Ward’s method. Finally, a GeoDetector-based iterative optimization process was implemented to refine the regionalization by maximizing alignment with historical flash flood distributions. Results show that all susceptibility models achieved excellent predictive performance (AUC > 0.95), with the CatBoost model trained on grid-based samples performing best (AUC = 0.997). The final regionalization map exhibits regional contiguity and effectively captures historical flood patterns, explaining 73% of their spatial variability. The integration of hybrid ML with explanatory feedback provides a novel perspective for generating susceptibility regionalization maps that are both expressive of flash flood risk and spatially coherent, in addition to providing support for exploring region-specific defense measures.

Keywords

Regionalization / Flash flood / Machine learning / Susceptibility prediction / Qinling Mountains

CRediT authorship contribution statement

Xinyue Ke: Writing - original draft, Methodology, Formal analysis. Ni Wang: Writing - review & editing, Funding acquisition, Conceptualization. Tianhao Li: Visualization, Data curation. Zheng Liu: Validation, Data curation. Zhiwei Li: Data curation. Ganggang Zuo: Writing - review & editing, Funding acquisition. Yiting Chen: Visualization, Funding acquisition.

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.

Acknowledgements

This work was supported by the Shaanxi Provincial Water Conservancy Science and Technology Plan Project (Program No. 2025slkj-10), the National Natural Science Foundation of China (Grant Nos. 51979221, 52309034) and the Natural Science Basic Research Program of Shaanxi (Program No. S2025-JC-QN-2416).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102213.

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