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Floods and erosion are natural hazards that present a substantial risks to both human and ecological systems, particularly in coastal regions. Flooding occurs when water inundates typically dry areas, causing widespread damage, while erosion gradually depletes soil and rock through processes driven by water and wind. This study proposes an innovative approach that integrates Deep Neural Decision Forest (DeepNDF), Feedforward Neural Network (FNN), autoencoders, and Bidirectional Recurrent Neural Networks (Bi-RNN) models for flood prediction, enhanced through transfer learning for erosion mapping in coastal environments. Utilizing multi-source datasets from the United States Geological Survey (USGS), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the National Algerian Institute of Cartography, and Sentinel-2 imagery, the key conditioning factors using Geographic Information System (GIS) were generated. The conditioning factors included elevation, slope, flow direction, curvature, distance from rivers, distance from roads, hillshade, topographic wetness index (TWI), stream power index (SPI), geology, and land use/land cover (LULC), as well as rainfall. To ensure the modeling reliability, the performance was rigorously evaluated using multiple statistical metrics, including the Area Under the Curve-Receiver Operating Characteristic (AUC-ROC), Precision, Recall, and F1 Score. The DeepNDF model achieved the highest performance for flood prediction with an AUC-ROC of 0.97, Precision of 0.93, Recall of 0.92, and an F1 Score of 0.925, while the transfer learning approach significantly improved erosion prediction, reaching an AUC-ROC of 0.92, Precision of 0.90, Recall of 0.92, and an F1 Score of 0.91. The analysis indicated that flood risks predominantly affected rangeland (18.68%) and bare ground (20.48%), while cropland was found to face the highest erosion risk, affecting approximately 3,471 km2. This research advances predictive modelling in hydrology and environmental science, providing valuable insights for disaster mitigation and resilience planning strategies in coastal areas.

来源平台：EARTH SCIENCE INFORMATICS