Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India
Abstract Flooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine...
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SpringerOpen
2025-01-01
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| Series: | Geoscience Letters |
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| Online Access: | https://doi.org/10.1186/s40562-025-00377-7 |
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| author | Devanantham Abijith Subbarayan Saravanan K S S Parthasarathy Nagireddy Masthan Reddy Janardhanam Niraimathi Ahmed Ali Bindajam Javed Mallick Maged Muteb Alharbi Hazem Ghassan Abdo |
| author_facet | Devanantham Abijith Subbarayan Saravanan K S S Parthasarathy Nagireddy Masthan Reddy Janardhanam Niraimathi Ahmed Ali Bindajam Javed Mallick Maged Muteb Alharbi Hazem Ghassan Abdo |
| author_sort | Devanantham Abijith |
| collection | DOAJ |
| description | Abstract Flooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. To identify flooded areas, the study utilised Google Earth Engine (GEE), Sentinel-1 data, and 12 geospatial datasets from multiple sources. A random forest algorithm was used for LULC change and flood susceptibility mapping. The LULC data are classified for the years 2000, 2010, and 2020, and from the classified data, the LULC for years 2030, 2040, and 2050 are projected for the study. Four future climate scenarios (SSP 126, 245, 370, and 585) were used for the average annual precipitation from the Coupled Model Intercomparison Project 6 (CMIP6). The results showed that the random forest model performed better in classifying LULC and identifying flood-prone areas. From the results, it has been depicted that the risk of flooding will increase across all scenarios over the period of 2000–2100, with some decadal fluctuations. A significant outcome indicates that the percentage of the area transitioning to moderate and very high flood risk consistently rises across all future projections. This study presents a viable method for flood susceptibility mapping based on different climate change scenarios and yields estimates of flood risk, which can provide valuable insights for managing flood risks. |
| format | Article |
| id | doaj-art-8d05c168bd5b46f392aa80f15fb2a8dd |
| institution | Kabale University |
| issn | 2196-4092 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Geoscience Letters |
| spelling | doaj-art-8d05c168bd5b46f392aa80f15fb2a8dd2025-02-02T12:27:41ZengSpringerOpenGeoscience Letters2196-40922025-01-0112112610.1186/s40562-025-00377-7Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, IndiaDevanantham Abijith0Subbarayan Saravanan1K S S Parthasarathy2Nagireddy Masthan Reddy3Janardhanam Niraimathi4Ahmed Ali Bindajam5Javed Mallick6Maged Muteb Alharbi7Hazem Ghassan Abdo8Department of Civil Engineering, National Institute of TechnologyDepartment of Civil Engineering, National Institute of TechnologyDepartment of Water Resources and Ocean Engineering, National Institute of Technology KarnatakaDepartment of Civil Engineering, National Institute of TechnologyDepartment of Civil Engineering, National Institute of TechnologyDepartment of Architecture, College of Architecture and Planning, King Khalid UniversityDepartment of Civil Engineering, College of Engineering, King Khalid UniversityMinistry of Environment, Water and Agriculture, Saudi Irrigation OrganizationGeography Department, Faculty of Arts and Humanities, Tartous UniversityAbstract Flooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. To identify flooded areas, the study utilised Google Earth Engine (GEE), Sentinel-1 data, and 12 geospatial datasets from multiple sources. A random forest algorithm was used for LULC change and flood susceptibility mapping. The LULC data are classified for the years 2000, 2010, and 2020, and from the classified data, the LULC for years 2030, 2040, and 2050 are projected for the study. Four future climate scenarios (SSP 126, 245, 370, and 585) were used for the average annual precipitation from the Coupled Model Intercomparison Project 6 (CMIP6). The results showed that the random forest model performed better in classifying LULC and identifying flood-prone areas. From the results, it has been depicted that the risk of flooding will increase across all scenarios over the period of 2000–2100, with some decadal fluctuations. A significant outcome indicates that the percentage of the area transitioning to moderate and very high flood risk consistently rises across all future projections. This study presents a viable method for flood susceptibility mapping based on different climate change scenarios and yields estimates of flood risk, which can provide valuable insights for managing flood risks.https://doi.org/10.1186/s40562-025-00377-7FloodLULCCMIP6Random forestTamil Nadu |
| spellingShingle | Devanantham Abijith Subbarayan Saravanan K S S Parthasarathy Nagireddy Masthan Reddy Janardhanam Niraimathi Ahmed Ali Bindajam Javed Mallick Maged Muteb Alharbi Hazem Ghassan Abdo Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India Geoscience Letters Flood LULC CMIP6 Random forest Tamil Nadu |
| title | Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India |
| title_full | Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India |
| title_fullStr | Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India |
| title_full_unstemmed | Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India |
| title_short | Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India |
| title_sort | assessing the impact of climate and land use change on flood vulnerability a machine learning approach in coastal region of tamil nadu india |
| topic | Flood LULC CMIP6 Random forest Tamil Nadu |
| url | https://doi.org/10.1186/s40562-025-00377-7 |
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