Geospatially interpretable long-term flood risk assessment using integrated weighting and LightGBM–SHAP
- 1Beijing City University, Beijing, China
- 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Abstract
Long-term flood risk assessment is vital for identifying persistent hazard-prone areas and supporting risk-informed spatial planning in rapidly urbanising regions. This study developed an interpretable geospatial framework integrating AHP, LightGBM–SHAP, and reliability-weighted game-theoretic weight fusion for the Beijing–Tianjin–Hebei (BTH) region using annual flood-related dataset for 2001–2025. Negative sample selection was refined using HAND-based low-likelihood screening and buffer-based exclusion around flood-labelled pixels. The LightGBM component showed strong internal predictive performance, with AUC, TSS, F1-score, and Kappa values of 0.92, 0.69, 0.84, and 0.69, respectively. Independent validation of the final flood risk index for 2022–2025 further confirmed its ability to identify flood-prone areas, with annual AUC values ranging from 0.63 to 0.83. High-risk areas were mainly concentrated in the central-eastern and southeastern plains, especially along the Beijing–Langfang–Tianjin–Cangzhou corridor. Flood risk fluctuated markedly and showed a weak increasing trend, with stable and increasing-risk areas accounting for 87.8% and 11.7%. Topographic factors dominated risk patterns, followed by hydrometeorological, urbanisation, and ecological factors. The framework supports long-term flood risk mapping, driver attribution, and spatial prioritisation of risk reduction measures.
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BibTeX
@article{yan2026geospatially,
title = {{Geospatially interpretable long-term flood risk assessment using integrated weighting and LightGBM–SHAP}},
author = {Yuna Yan and Junru Jia and Qian Li and Nina Xiong and Zhenyu Li and Xiaofan Li},
journal = {Geomatics, Natural Hazards \& Risk},
year = {2026},
month = oct,
doi = {10.1080/19475705.2026.2751763},
url = {https://doi.org/10.1080/19475705.2026.2751763},
note = {CC BY},
}
RIS
TY - JOUR
TI - Geospatially interpretable long-term flood risk assessment using integrated weighting and LightGBM–SHAP
AU - Yuna Yan
AU - Junru Jia
AU - Qian Li
AU - Nina Xiong
AU - Zhenyu Li
AU - Xiaofan Li
PY - 2026
DA - 2026/10/30
JO - Geomatics, Natural Hazards & Risk
DO - 10.1080/19475705.2026.2751763
UR - https://doi.org/10.1080/19475705.2026.2751763
AB - Long-term flood risk assessment is vital for identifying persistent hazard-prone areas and supporting risk-informed spatial planning in rapidly urbanising regions. This study developed an interpretable geospatial framework integrating AHP, LightGBM–SHAP, and reliability-weighted game-theoretic weight fusion for the Beijing–Tianjin–Hebei (BTH) region using annual flood-related dataset for 2001–2025. Negative sample selection was refined using HAND-based low-likelihood screening and buffer-based exclusion around flood-labelled pixels. The LightGBM component showed strong internal predictive performance, with AUC, TSS, F1-score, and Kappa values of 0.92, 0.69, 0.84, and 0.69, respectively. Independent validation of the final flood risk index for 2022–2025 further confirmed its ability to identify flood-prone areas, with annual AUC values ranging from 0.63 to 0.83. High-risk areas were mainly concentrated in the central-eastern and southeastern plains, especially along the Beijing–Langfang–Tianjin–Cangzhou corridor. Flood risk fluctuated markedly and showed a weak increasing trend, with stable and increasing-risk areas accounting for 87.8% and 11.7%. Topographic factors dominated risk patterns, followed by hydrometeorological, urbanisation, and ecological factors. The framework supports long-term flood risk mapping, driver attribution, and spatial prioritisation of risk reduction measures.
SN - 1947-5713
SN - 1947-5705
ER -