FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in cities
The study introduces FloodGenome, an interpretable machine learning model, to assess flood risk disposition in urban areas by analyzing hydrological, topographic, and built-environment features and their interactions. Utilizing data from the U.S. National Flood Insurance Program (2003–2023) across f...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research: Infrastructure and Sustainability |
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| Online Access: | https://doi.org/10.1088/2634-4505/adb800 |
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| _version_ | 1850182011285667840 |
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| author | Chenyue Liu Ali Mostafavi |
| author_facet | Chenyue Liu Ali Mostafavi |
| author_sort | Chenyue Liu |
| collection | DOAJ |
| description | The study introduces FloodGenome, an interpretable machine learning model, to assess flood risk disposition in urban areas by analyzing hydrological, topographic, and built-environment features and their interactions. Utilizing data from the U.S. National Flood Insurance Program (2003–2023) across four metropolitan areas, it employs k-means clustering and a random forest model to classify and predict property flood risk levels. The model’s effectiveness is proven across different metropolitan areas, highlighting the importance of factors like elevation, and impervious surfaces in determining flood risk. FloodGenome’s analysis aids in evaluating future urban development impacts on flood risk and refining property flood risk assessments at a detailed level. This tool offers critical insights for flood risk management, supporting the development of urban design strategies to mitigate flood risks. |
| format | Article |
| id | doaj-art-e42a8031fe964b6287442146a5f396f2 |
| institution | OA Journals |
| issn | 2634-4505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research: Infrastructure and Sustainability |
| spelling | doaj-art-e42a8031fe964b6287442146a5f396f22025-08-20T02:17:46ZengIOP PublishingEnvironmental Research: Infrastructure and Sustainability2634-45052025-01-015101501810.1088/2634-4505/adb800FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in citiesChenyue Liu0https://orcid.org/0000-0003-3528-8721Ali Mostafavi1Zachry Department of Civil and Environmental Engineering, Urban Resilience.AI Laboratory, Texas A&M University , College Station, TX, United States of AmericaZachry Department of Civil and Environmental Engineering, Urban Resilience.AI Laboratory, Texas A&M University , College Station, TX, United States of AmericaThe study introduces FloodGenome, an interpretable machine learning model, to assess flood risk disposition in urban areas by analyzing hydrological, topographic, and built-environment features and their interactions. Utilizing data from the U.S. National Flood Insurance Program (2003–2023) across four metropolitan areas, it employs k-means clustering and a random forest model to classify and predict property flood risk levels. The model’s effectiveness is proven across different metropolitan areas, highlighting the importance of factors like elevation, and impervious surfaces in determining flood risk. FloodGenome’s analysis aids in evaluating future urban development impacts on flood risk and refining property flood risk assessments at a detailed level. This tool offers critical insights for flood risk management, supporting the development of urban design strategies to mitigate flood risks.https://doi.org/10.1088/2634-4505/adb800urban floodingrandom forestSHAP analysis |
| spellingShingle | Chenyue Liu Ali Mostafavi FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in cities Environmental Research: Infrastructure and Sustainability urban flooding random forest SHAP analysis |
| title | FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in cities |
| title_full | FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in cities |
| title_fullStr | FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in cities |
| title_full_unstemmed | FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in cities |
| title_short | FloodGenome: interpretable machine learning for decoding features shaping property flood risk predisposition in cities |
| title_sort | floodgenome interpretable machine learning for decoding features shaping property flood risk predisposition in cities |
| topic | urban flooding random forest SHAP analysis |
| url | https://doi.org/10.1088/2634-4505/adb800 |
| work_keys_str_mv | AT chenyueliu floodgenomeinterpretablemachinelearningfordecodingfeaturesshapingpropertyfloodriskpredispositionincities AT alimostafavi floodgenomeinterpretablemachinelearningfordecodingfeaturesshapingpropertyfloodriskpredispositionincities |