Enhancing flood prediction through remote sensing, machine learning, and Google Earth Engine - Sonia Hajji; Samira Krimissa; Kamal Abdelrahman; Abdelghani Boudhar; Abdelghani Boudhar; Abdelghani Boudhar; Abdenbi Elaloui; Maryam Ismaili; Meryem El Bouzekraoui; Mohamed Chikh Essbiti; Ali Y. Kahal; Biraj Kanti Mondal; et al. (Frontiers in Water)
Floods are the most common natural hazard, causing major economic losses and severely affecting people’s lives. Therefore, accurately identifying vulnerable areas is crucial for saving lives and resources, particularly in regions with restricted access and insufficient data. The aim of this study was to automate the identification of flood-prone areas within a data-scarce, mountainous watershed using remote sensing (RS) and machine learning (ML) models. In this study, we integrate the Normalized Difference Flood Index (NDFI), using Google Earth Engine to generate flood inventory, which is considered a crucial step in flood susceptibility mapping. Seventeen determining factors, namely, elevation, slope, aspect, curvature, the Stream Power Index (SPI), the Topographic Wetness Index (TWI), the Topographic Ruggedness Index (TRI), the Topographic Position Index (TPI), distance from roads, distance from rivers, stream density, rainfall, lithology, the Normalized Difference Vegetation Index (NDVI), land use, length slope (LS) factor, and the Convergence Index were used to map the flood vulnerability. This study aimed to assess the predictive performance of gradient boosting, AdaBoost, and random forest. The model performance was evaluated using the area under the curve (AUC). The performance assessment results showed that random forest (RF) achieved the highest accuracy (1), followed by random forest and gradient boosting ensemble (RF-GB) (0.96), gradient boosting (GB) (0.95), and AdaBoost (AdaB) (0.83). Additionally, in this research study, we employed the Shapely Additive Explanations (SHAP) method, to explain machine learning model predictions and determine the most contributing factor in each model. This study introduces a novel approach to generate flood inventory, providing significant insights into flood susceptibility mapping, and offering potential pathways for future research and practical applications. Overall, the research emphasizes the need to integrate urban planning with emergency preparedness to build safer and more resilient communities.