A deep convolutional neural network model for rapid prediction of fluvial flood inundation

TL;DR

This paper introduces a deep CNN model trained on 2D hydraulic model outputs for rapid, accurate flood inundation prediction, outperforming traditional methods and enabling real-time flood forecasting.

Contribution

The paper presents a novel CNN-based approach for fast flood inundation prediction, demonstrating superior accuracy and efficiency over existing regression methods.

Findings

CNN outperforms SVR significantly

Error in flood depth prediction is below 0.5 meters for major events

Model achieves over 99% accuracy in flooded cell detection

Abstract

Most of the two-dimensional (2D) hydraulic/hydrodynamic models are still computationally too demanding for real-time applications. In this paper, an innovative modelling approach based on a deep convolutional neural network (CNN) method is presented for rapid prediction of fluvial flood inundation. The CNN model is trained using outputs from a 2D hydraulic model (i.e. LISFLOOD-FP) to predict water depths. The pre-trained model is then applied to simulate the January 2005 and December 2015 floods in Carlisle, UK. The CNN predictions are compared favourably with the outputs produced by LISFLOOD-FP. The performance of the CNN model is further confirmed by benchmarking against a support vector regression (SVR) method. The results show that the CNN model outperforms SVR by a large margin. The CNN model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices. The estimated error for reproducing maximum flood depth is 0 ~ 0.2 meters for the 2005 event and 0 ~ 0.5 meters for the 2015 event at over 99% of the cells covering the computational domain. The proposed CNN method offers great potential for real-time flood modelling/forecasting considering its simplicity, superior performance and computational efficiency.