Aggregated flood susceptibility mapping in Upper Chao Phraya River Basin using Shannon’s Entropy, Machine Learning, and Stacking ensemble methods - Gen Long; Sarintip Tantanee; K Nusit; P. Sooraksa (Geomatics Natural Hazards and Risk, 2026)
Flood susceptibility mapping in large and heterogeneous basins requires methods capable of representing spatial variability that conventional basin-wide models often overlook. This study develops a sub-basin aggregation framework for the Upper Chao Phraya River Basin, Thailand, integrating localized susceptibility modelling into a unified basin-scale product. Thirteen flood conditioning factors were initially selected and objectively weighted using Shannon’s Entropy (SE), which reduced to 6–9 distinct hydrological drivers in the basin and each sub-basin. Nine machine learning algorithms (RF, KNN, SVM, DT, LR, ANN, NB, CART, and MLP) and a Stacking ensemble were applied to each basin, followed by three aggregation strategies: (1) SE-based sub-basin aggregation, (2) Stacking-based aggregation, and (3) best ML-based sub-basin aggregation. Results show that aggregated models outperform single basin-wide models, with the best ML-based aggregation achieving the highest accuracy (AUC = 0.973). Meanwhile, the SE-based aggregation produced the most balanced susceptibility map (44.3% Very Low; 15.7% Very High), highlighting a trade-off between predictive performance and spatial realism. The framework effectively captures sub-basin heterogeneity—for instance, Curvature was dominant in the Wang sub-basin, whereas Elevation prevailed elsewhere. Overall, the proposed aggregation strategy offers a scalable and transferable approach for large-scale modelling and supports data-driven flood management in complex river systems.
Keywords: Flood Risk Assessment and Management; Hydrology and Watershed Management Studies; Hydrology and Drought Analysis