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Deep learning neural networks for spatially explicit prediction of flash flood probability
Geoscience Frontiers ( IF 8.9 ) Pub Date : 2020-12-16 , DOI: 10.1016/j.gsf.2020.09.007
Mahdi Panahi , Abolfazl Jaafari , Ataollah Shirzadi , Himan Shahabi , Omid Rahmati , Ebrahim Omidvar , Saro Lee , Dieu Tien Bui

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 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.



中文翻译:

深度学习神经网络用于空间泛滥预测山洪暴发概率

洪水概率图对于一系列应用都是必不可少的,包括土地使用规划,制定缓解策略和预警系统。这项研究描述了深度学习神经网络的两种架构(卷积神经网络(CNN)和递归神经网络(RNN))在空间泛滥预测和洪水泛滥概率测绘中的潜在应用。为了开发和验证预测模型,构建了一个地理空间数据库,其中包含伊朗北部Golestan省的历史洪水事件和地质环境特征的记录。采用逐步权重评估比率分析法(SWARA)研究洪水与不同影响因素之间的空间相互作用。CNN和RNN模型使用SWARA权重进行了训练,并使用接收器工作特性技术进行了验证。结果表明,在预测未来洪水时,CNN模型(AUC = 0.832,RMSE = 0.144)的性能略好于RNN模型(AUC = 0.814,RMSE = 0.181)。此外,与先前在同一研究区域中使用不同模型的研究相比,这些模型显示了对洪水的改进预测。这项研究表明,空间显式深度学习神经网络模型成功地捕获了Golestan省洪水概率空间模式的异质性,并且所得的概率图可用于制定应对未来洪水的缓解计划。我们研究的一般政策含义表明,设计,实施,

更新日期:2020-12-16
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