A comprehensive review of flood-prone area zonation using ensemble and hybrid machine learning models with a framework proposal for modelling - Gen Long; Sarintip Tantanee; Korakod Nusit; Pitikhate Sooraksa (Geocarto International, 2025)
This research offers a systematic review encompassing 63 relevant peer-reviewed papers concerning flood susceptibility, hazard and risk assessment using various ensemble machine learning and hybrid approaches. It examines publication details, study characteristics, terminology, flood inventories, conditioning factors, data resolution, and modeling approaches. A key contribution is a proposed framework for practising ensemble or hybrid modeling. The framework comprises data preparation, checking for multicollinearity, factor selection and weighting, optional factor optimization, k-fold cross validation where appropriate, ensemble or hybrid modeling, and model evaluation. This framework aims to facilitate research activities and enhance model quality. Furthermore, the statistical outcomes can benefit researchers by guiding their further research. The knowledge produced by this study will thus help advance understanding of the application of ensemble machine learning and hybrid methods for the zonation of flood-prone areas and guide the direction of further research on enhancing the effectiveness of flood risk management strategies.
Keywords: Flood Risk Assessment and Management; Hydrology and Watershed Management Studies; Hydrological Forecasting Using AI