Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development

Abstract

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

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Conditional Generative Adversarial Nets
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• DGM: A deep learning algorithm for solving partial differential equations
• Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
• Accelerating Eulerian Fluid Simulation With Convolutional Networks

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