Deep learning neural networks for spatially explicit prediction of flash flood probability

• Published in: 地学前缘（英文版） 2021, Vol.12 (3), p.370-383
• Main Authors: Mahdi Panahi, Abolfazl Jaafari, Ataollah Shirzadi, Himan Shahabi, Omid Rahmati, Ebrahim Omidvar, Saro Lee, Dieu Tien Bui
• Format: Article
• Language: English
• ISSN: 1674-9871

Flood probability maps are essential for a range of applications, including land use planning and developing mitigation strategies and early warning systems. This study describes the potential application of two architec-tures of deep learning neural networks, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), for spatially explicit prediction and mapping of flash flood probability. To develop and vali-date the predictive models, a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed. The step-wise weight assessment ratio analysis (SWARA) was employed to investigate the spatial interplay between floods and different influencing factors. The CNN and RNN models were trained using the SWARA weights and val-idated using the receiver operating characteristics technique. The results showed that the CNN model (AUC=0.832, RMSE = 0.144) performed slightly better than the RNN model (AUC = 0.814, RMSE = 0.181) in predicting future floods. Further, these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area. This study showed that the spatially ex-plicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province, and the resulting probability maps can be used for the develop-ment of mitigation plans in response to the future floods. The general policy implication of our study suggests that design, implementation, and verification of flood early warning systems should be directed to approxi-mately 40％of the land area characterized by high and very susceptibility to flooding.