物理信息数据驱动的洪水预测模型--深度学习在城市洪水发展预测中的应用
Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development
摘要：
城市地区的山洪暴发越来越频繁。检测这些洪水将大大有助于减轻人类和经济损失。然而，目前的洪水预报方法要么过于缓慢，要么过于简单，以捕捉洪水发展的细节。使用深度神经网络，这项工作的目的是提高基于物理的2-D城市洪水预测方法的计算速度，由浅水方程（SWE）控制。采用卷积神经网络（CNN）和条件生成对抗神经网络（cGANs）从偏微分方程（PDE）模拟数据中提取洪水的动力学信息。从均方误差（MSE）和峰值信噪比（PSNR）两个方面对数据驱动模型的性能进行了评价。基于深度学习的数据驱动的洪水预测模型被证明能够提供洪水发展的精确实时预测
英文摘要：
Flash floods in urban areas occur with increasing frequency. Detecting these floods would greatlyhelp alleviate human and economic losses. However, current flood prediction methods are eithertoo slow or too simplified to capture the flood development in details. Using Deep Neural Networks,this work aims at boosting the computational speed of a physics-based 2-D urban flood predictionmethod, governed by the Shallow Water Equation (SWE). Convolutional Neural Networks(CNN)and conditional Generative Adversarial Neural Networks(cGANs) are applied to extract the dy-namics of flood from the data simulated by a Partial Differential Equation(PDE) solver. Theperformance of the data-driven model is evaluated in terms of Mean Squared Error(MSE) andPeak Signal to Noise Ratio(PSNR). The deep learning-based, data-driven flood prediction modelis shown to be able to provide precise real-time predictions of flood development
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