Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm
Thailand's capital Bangkok is no stranger to floods and the disruption caused by repetitive flooding. Rapid urbanization, inadequate infrastructure, and climate change exacerbate the situation as urban flooding becomes increasingly more frequent and severe. The aim of this study is to assess fu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Environmental and Sustainability Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972724002277 |
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| author | Duangporn Garshasbi Jarunya Kitiphaisannon Tanaphoom Wongbumru Nawhath Thanwiset Thanvisitthpon |
| author_facet | Duangporn Garshasbi Jarunya Kitiphaisannon Tanaphoom Wongbumru Nawhath Thanwiset Thanvisitthpon |
| author_sort | Duangporn Garshasbi |
| collection | DOAJ |
| description | Thailand's capital Bangkok is no stranger to floods and the disruption caused by repetitive flooding. Rapid urbanization, inadequate infrastructure, and climate change exacerbate the situation as urban flooding becomes increasingly more frequent and severe. The aim of this study is to assess future urban flood risk of Bangkok metropolitan at the district level for three future periods: 2033, 2043, and 2053. In the assessment of flood risk, the future values of six dynamic urban flood indicators are first projected using an integrative geoprocessing and random forest machine learning algorithm. The projected future indicator values are subsequently used to assess urban flood risk across Bangkok's 50 districts. The six dynamic indicators of urban flood risk are average monthly rainfall, wet days, vegetation cover, population density, flood waste, and anti-flood infrastructure. The findings indicate a steady increase in average monthly rainfall and wet days, highlighting the need for improved floodwater drainage systems and flood resilience. Ongoing urbanization and decreasing vegetation cover exacerbate flood risks. Densely populated areas remain highly susceptible to flooding, underscoring the significance of effective population and waste management strategies. This study also proposes three-timescale urban flood mitigation plans (10-, 20- and 30-year plans) to mitigate future urban flood risk, focusing on short-, medium-, and long-term measures. This research is the first to integrate geoprocessing with machine learning to enhance the prediction performance and accuracy of future urban flood risk projections. |
| format | Article |
| id | doaj-art-8895150b8b0c4e5da90e5c4e0a546590 |
| institution | Kabale University |
| issn | 2665-9727 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj-art-8895150b8b0c4e5da90e5c4e0a5465902025-01-29T05:01:48ZengElsevierEnvironmental and Sustainability Indicators2665-97272025-02-0125100559Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithmDuangporn Garshasbi0Jarunya Kitiphaisannon1Tanaphoom Wongbumru2Nawhath Thanwiset Thanvisitthpon3Environment and Safety Management Program, Faculty of Science, Chandrakasem Rajabhat University, ThailandEnvironment and Safety Management Program, Faculty of Science, Chandrakasem Rajabhat University, ThailandSustainable Community and Urban Health Unit (SC UNIT), Rajamangala University of Technology Thanyaburi (RMUTT), ThailandSustainable Community and Urban Health Unit (SC UNIT), Rajamangala University of Technology Thanyaburi (RMUTT), Thailand; Corresponding author.Thailand's capital Bangkok is no stranger to floods and the disruption caused by repetitive flooding. Rapid urbanization, inadequate infrastructure, and climate change exacerbate the situation as urban flooding becomes increasingly more frequent and severe. The aim of this study is to assess future urban flood risk of Bangkok metropolitan at the district level for three future periods: 2033, 2043, and 2053. In the assessment of flood risk, the future values of six dynamic urban flood indicators are first projected using an integrative geoprocessing and random forest machine learning algorithm. The projected future indicator values are subsequently used to assess urban flood risk across Bangkok's 50 districts. The six dynamic indicators of urban flood risk are average monthly rainfall, wet days, vegetation cover, population density, flood waste, and anti-flood infrastructure. The findings indicate a steady increase in average monthly rainfall and wet days, highlighting the need for improved floodwater drainage systems and flood resilience. Ongoing urbanization and decreasing vegetation cover exacerbate flood risks. Densely populated areas remain highly susceptible to flooding, underscoring the significance of effective population and waste management strategies. This study also proposes three-timescale urban flood mitigation plans (10-, 20- and 30-year plans) to mitigate future urban flood risk, focusing on short-, medium-, and long-term measures. This research is the first to integrate geoprocessing with machine learning to enhance the prediction performance and accuracy of future urban flood risk projections.http://www.sciencedirect.com/science/article/pii/S2665972724002277Climate adaptationUrbanization impactFlood resilienceDynamic indicatorsWater drainage systemsGreen infrastructure |
| spellingShingle | Duangporn Garshasbi Jarunya Kitiphaisannon Tanaphoom Wongbumru Nawhath Thanwiset Thanvisitthpon Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm Environmental and Sustainability Indicators Climate adaptation Urbanization impact Flood resilience Dynamic indicators Water drainage systems Green infrastructure |
| title | Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm |
| title_full | Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm |
| title_fullStr | Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm |
| title_full_unstemmed | Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm |
| title_short | Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm |
| title_sort | assessment of future urban flood risk of thailand s bangkok metropolis using geoprocessing and machine learning algorithm |
| topic | Climate adaptation Urbanization impact Flood resilience Dynamic indicators Water drainage systems Green infrastructure |
| url | http://www.sciencedirect.com/science/article/pii/S2665972724002277 |
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