A physics-guided neural network for flooding area detection using SAR imagery and local river gauge observations

TL;DR

This paper introduces a physics-guided neural network that leverages SAR imagery and river water level data to accurately detect flooding areas, outperforming existing methods across multiple study regions.

Contribution

The study presents a novel neural network model that integrates physical water level data with SAR imagery for improved flood detection accuracy.

Findings

Achieved IoU of 0.89 for water detection and 0.96 for non-water areas.

Outperformed other unsupervised methods, especially during low water levels.

Effective across five different study regions.

Abstract

The flooding extent area in a river valley is related to river gauge observations. The higher the water elevation, the larger the flooding area. Due to synthetic aperture radar\textquoteright s (SAR) capabilities to penetrate through clouds, radar images have been commonly used to estimate flooding extent area with various methods, from simple thresholding to deep learning models. In this study, we propose a physics-guided neural network for flooding area detection. Our approach takes as input data the Sentinel 1 time-series images and the water elevations in the river assigned to each image. We apply the Pearson correlation coefficient between the predicted sum of water extent areas and the local water level observations of river water elevations as the loss function. The effectiveness of our method is evaluated in five different study areas by comparing the predicted water maps with reference water maps obtained from digital terrain models and optical satellite images. The highest Intersection over Union (IoU) score achieved by our models was 0.89 for the water class and 0.96 for the non-water class. Additionally, we compared the results with other unsupervised methods. The proposed neural network provided a higher IoU than the other methods, especially for SAR images registered during low water elevation in the river.