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

08/23/2019
by   Kun Qian, et al.
0

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

READ FULL TEXT

page 24

page 25

page 27

page 28

page 30

page 32

page 33

page 34

research
10/07/2022

Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs

Prediction error quantification in machine learning has been left out of...
research
10/09/2021

Predicting the spread of COVID-19 in Delhi, India using Deep Residual Recurrent Neural Networks

Detecting the spread of coronavirus will go a long way toward reducing h...
research
09/01/2022

STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

High-performance traffic flow prediction model designing, a core technol...
research
04/17/2020

Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-...
research
02/20/2023

An evaluation of deep learning models for predicting water depth evolution in urban floods

In this technical report we compare different deep learning models for p...
research
05/11/2020

Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

The heterogeneous microstructure in metallic components results in local...
research
04/08/2022

Evaluating the Adversarial Robustness for Fourier Neural Operators

In recent years, Machine-Learning (ML)-driven approaches have been widel...

Please sign up or login with your details

Forgot password? Click here to reset