Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge

Wang, Jialou; Sanderson, Jacob; Iqbal, Sadaf; Woo, Wai Lok

doi:10.3390/rs17091540

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Open AccessArticle

Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge

Department of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UK

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Remote Sens. 2025, 17(9), 1540; https://doi.org/10.3390/rs17091540 (registering DOI)

Submission received: 12 March 2025 / Revised: 17 April 2025 / Accepted: 22 April 2025 / Published: 26 April 2025

(This article belongs to the Special Issue Remote Sensing Data for Modeling and Managing Natural Disasters)

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Abstract

Flooding is one of the most devastating natural disasters worldwide, with increasing frequency due to climate change. Traditional hydrological models require extensive data and computational resources, while machine learning (ML) models struggle to capture spatial dependencies. To address this, we propose a modified U-Net architecture that integrates prior hydrological knowledge of permanent water bodies to improve flood susceptibility mapping in Northumberland County, UK. By embedding domain-specific insights, our model achieves a higher area under the curve (AUC) (0.97) compared to the standard U-Net (0.93), while also reducing training time by converging three times faster. Additionally, we integrate a Grad-CAM module to provide visualisations explaining the areas of attention from the model, enabling interpretation of its decision-making, thus reducing barriers to its practical implementation.

Keywords: flood susceptibility mapping; deep learning; U-Net; hydrology-aware deep learning; remote sensing; digital terrain model (DTM); explainable AI (XAI); Grad-CAM

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MDPI and ACS Style

Wang, J.; Sanderson, J.; Iqbal, S.; Woo, W.L. Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge. Remote Sens. 2025, 17, 1540. https://doi.org/10.3390/rs17091540

AMA Style

Wang J, Sanderson J, Iqbal S, Woo WL. Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge. Remote Sensing. 2025; 17(9):1540. https://doi.org/10.3390/rs17091540

Chicago/Turabian Style

Wang, Jialou, Jacob Sanderson, Sadaf Iqbal, and Wai Lok Woo. 2025. "Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge" Remote Sensing 17, no. 9: 1540. https://doi.org/10.3390/rs17091540

APA Style

Wang, J., Sanderson, J., Iqbal, S., & Woo, W. L. (2025). Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge. Remote Sensing, 17(9), 1540. https://doi.org/10.3390/rs17091540

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Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge

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