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

TL;DR

This paper presents a physics-informed deep learning model combining CNNs and cGANs to enable fast, real-time urban flood prediction by approximating solutions to the Shallow Water Equation, improving speed and accuracy over traditional methods.

Contribution

It introduces a novel physics-informed deep learning approach that accelerates urban flood prediction while maintaining high accuracy, integrating PDE-based simulations with neural networks.

Findings

Deep learning models achieve low MSE and high PSNR in flood prediction

The approach provides real-time flood development forecasts

Models outperform traditional physics-based methods in speed and accuracy

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