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标题：Deep learning neural networks for spatially explicit prediction of flash flood probability
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作者：Mahdi Panahi ; Abolfazl Jaafari ; Ataollah Shirzadi 等
期刊名称：Geoscience Frontiers
印刷版ISSN：1674-9871
出版年度：2021
卷号：12
期号：3
页码：1-14
DOI：10.1016/j.gsf.2020.09.007
语种：English
出版社：Elsevier
摘要：AbstractFlood 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 architectures 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 validate 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 validated 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 explicit 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 development 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 approximately 40% of the land area characterized by high and very susceptibility to flooding.Graphical abstractDisplay OmittedHighlights•CNN and RNN deep learnings were adopted for flood probability mapping.•CNN (AUC = 0.832) performed slightly better than RNN (AUC = 0.814).•Very low to very high probabilities of flood occurrences were delineated.•Flood prevention activities primarily needed on 40% of the land area.
关键词：KeywordsSpatial modelingMachine learningConvolutional neural networksRecurrent neural networksGISIran