ArticleScientific reports2025
Integrating numerical models with deep learning techniques for flood risk assessment.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Floods are among the natural disasters that pose significant threats to both urban and rural infrastructure, as well as the lives and properties of individuals. Streamflow prediction is essential for obtaining hydrological information and is critical for a variety of water resource projects. While precise daily streamflow predictions are indispensable, forecasting streamflow according to the limited data can help reduce computational time and enhance the efficacy of flood early warning systems. The purpose of this research is streamflow forecasting with the Long Short-Term Memory (LSTM) approach for the next 20 years. The peak streamflow extracted from the LSTM model was entered into HEC-RAS software and obtained flood zone maps and hazard maps. Furthermore, the effectiveness of the proposed method was assessed through statistical analysis, including the coefficient of determination (R
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