Flood susceptibility zonation mapping: A critical review of GIS, remote sensing, and machine learning approaches
DOI:
https://doi.org/10.12775/EQ.2026.050Keywords
flood hazard, susceptibility, zonation mapping, remote sensing and GIS, machine learning modelAbstract
Floods are among the most frequent and destructive natural disasters, causing severe damage to infrastructure, property, and human lives worldwide. Flood susceptibility has become an essential tool for minimising flood impacts and supporting disaster management planning. This paper discusses the use of geographic information system (GIS) and remote sensing methods for flood susceptibility assessment, focusing on conditioning variables, modeling methodologies, and prediction performance. The review comprehensively examined published research, including spatial factors such as elevation, slope, rainfall, land use and land cover (LULC), soil properties, drainage density, proximity to rivers, and topographic and hydrological indices. The use of optical and radar satellite images for land-cover and temporal flood analysis and flood extent mapping was also evaluated. The review highlights the application of advanced approaches including the analytical hierarchy process (AHP), multi-criteria decision method (MCDM), logistic regression (LR), frequency ratio (FR), fuzzy logic, artificial neural network (ANN), and machine learning (ML), and convolutional neural network (CNN) methods for flood susceptibility zonation mapping. The finding indicates, when it comes to flood susceptibility mapping, CNN and advanced machine learning models typically achieve accuracy between 85%-97%. Logistic regression and frequency ratio have accuracies of 78%-92% and 75%-90%, respectively. AHP brings up the rear at 70%-85%. In addition, hydrological and hydraulic models such as Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Center – River Analysis System (HEC-RAS) provide a process-based understanding of rainfall-runoff transformation and flood propagation, with model performance commonly evaluated using the Nash–Sutcliffe Efficiency (NSE) and coefficient of determination (R²). The results suggest that ML and deep-learning models are potent forecasting tools for flood susceptibility zonation, although physically based hydrological and hydraulic models provide a complementary understanding of flood dynamics. The study indicates that the combined use of GIS, remote sensing, data-driven models, and process-based hydrological modeling may increase the reliability of flood susceptibility assessment and facilitate effective flood mitigation, early warning, disaster preparation, and sustainable land-use planning.
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