Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development
This work addresses the problem of slow or simplified flood prediction methods for urban areas, offering a faster alternative to alleviate human and economic losses, though it is incremental as it builds on existing physics-based models.
The paper tackled urban flood prediction by using deep neural networks to accelerate a physics-based 2-D model governed by the Shallow Water Equation, achieving precise real-time predictions as evaluated by MSE and PSNR metrics.
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