چکیده مقاله
Urban flooding presents a major challenge to infrastructure resilience and public safety in rapidly growing cities including Tehran, the capital city of Iran This study introduces an explainable deep learning framework for flood susceptibility mapping using Convolutional Neural Networks CNNs trained on multi source geospatial data Thirteen spatial input layers—including DEM, slope, aspect, curvature, TWI, TRI, TPI, SPI, SLF, land use, distance to rivers and drainage networks, NDVI, and precipitation—were used to extract 64×64 pixel patches for model training Flood inundation maps generated from Sentinel 1 imagery were used as reference labels across multiple flood and non flood events The model was optimized with focal loss to address class imbalance Its performance was evaluated using the Area Under the ROC Curve AUC , achieving values above 0 95, indicating excellent predictive capability Model interpretability was enhanced using SHAP and Grad CAM, which revealed the spatial importance of input features Finally, the flood susceptibility map of Tehran’s administrative boundary was produced, providing a valuable tool for flood risk assessment and urban planning
کلیدواژهها
نویسندگان
Mahdi Amerehei
Faculty of Environment, University of Tehran, Tehran, Iran
Bentolhoda Asl-Rousta
Faculty of Environment, University of Tehran, Tehran, Iran
Mohammad Hossein Niksokhan
Faculty of Environment, University of Tehran, Tehran, Iran
شیوه ارجاع
Amerehei, Mahdi and Asl-Rousta, Bentolhoda and Niksokhan, Mohammad Hossein,1404,Urban Flood Susceptibility Mapping with Convolutional Neural Network and Explainable AI: Tehran Case Study,The Third National Conference on Water Quality Management and the Fifth National Conference on Water Consumption Management with a Waste Reduction and Recycling Approach,Tehran
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