A deep learning model for predicting river flood depth and extent. (November 2021)

• Record Type: Journal Article
• Authors: Hosseiny, Hossein
• Is Part Of: Environmental modelling & software. Volume 145(2021)
• Publication Date: 2021-11
• Subjects: Flood inundation modeling -- River hydraulics -- Machine learning -- Deep learning -- Convolutional neural networks -- U-Net; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105186
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper presents an innovative deep learning (DL) framework to (a) automatically identify river geometry and flood extent, and (b) predict river flooding depth. To do that, U-Net, an advanced convolutional neural network (CNN), was modified and given the designation of U-NetRiver . With the modification, the model received an input composite image with two bands of ground elevation and flooding discharge, and the output was water depth. The model was trained and validated based on the outputs from iRIC (a two-dimensional hydraulic model) for a segment of the Green River in the state of Utah. The results showed that the U-NetRiver could identify the river shape and wetted areas for flooded regions automatically. The maximum difference of predicted river depth obtained from U-NetRiver and the one obtained from the hydraulic model was 2.7 m. This result suggests a 29% improvement in prediction of the maximum flood depth in the river. Highlights: U-NetRiver, could successfully predict river flooding depth and extent. The model accurately and automatically detected river geometry and shape. The model is more objective as it minimized human involvement in flood prediction. The model decreased the error in predicted river maximum flooding depth by 29%.