Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge
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 p...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1540 |
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| author | Jialou Wang Jacob Sanderson Sadaf Iqbal Wai Lok Woo |
| author_facet | Jialou Wang Jacob Sanderson Sadaf Iqbal Wai Lok Woo |
| author_sort | Jialou Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dee32ea033b44f339461d88d7b12c2d6 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dee32ea033b44f339461d88d7b12c2d62025-08-20T01:49:50ZengMDPI AGRemote Sensing2072-42922025-04-01179154010.3390/rs17091540Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior KnowledgeJialou Wang0Jacob Sanderson1Sadaf Iqbal2Wai Lok Woo3Department of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UKDepartment of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UKDepartment of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UKDepartment of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UKFlooding 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.https://www.mdpi.com/2072-4292/17/9/1540flood susceptibility mappingdeep learningU-Nethydrology-aware deep learningremote sensingdigital terrain model (DTM) |
| spellingShingle | Jialou Wang Jacob Sanderson Sadaf Iqbal Wai Lok Woo Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge Remote Sensing flood susceptibility mapping deep learning U-Net hydrology-aware deep learning remote sensing digital terrain model (DTM) |
| title | Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge |
| title_full | Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge |
| title_fullStr | Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge |
| title_full_unstemmed | Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge |
| title_short | Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge |
| title_sort | accelerated and interpretable flood susceptibility mapping through explainable deep learning with hydrological prior knowledge |
| topic | flood susceptibility mapping deep learning U-Net hydrology-aware deep learning remote sensing digital terrain model (DTM) |
| url | https://www.mdpi.com/2072-4292/17/9/1540 |
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