Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya
Reliable flood monitoring and prediction remain a challenge in data-scarce regions, particularly in arid and semi-arid environments. This study explores the integration of remote sensing data and machine learning techniques to improve flood detection and early warning capabilities in Lodwar Town of...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Frontiers Media
2025
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| Online Access: | https://hdl.handle.net/10568/177234 |
| _version_ | 1855531310756397056 |
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| author | Lakew, Haileyesus Belay Taye, Meron Teferi Lino, O. Dyer, E. |
| author_browse | Dyer, E. Lakew, Haileyesus Belay Lino, O. Taye, Meron Teferi |
| author_facet | Lakew, Haileyesus Belay Taye, Meron Teferi Lino, O. Dyer, E. |
| author_sort | Lakew, Haileyesus Belay |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Reliable flood monitoring and prediction remain a challenge in data-scarce regions, particularly in arid and semi-arid environments. This study explores the integration of remote sensing data and machine learning techniques to improve flood detection and early warning capabilities in Lodwar Town of the Turkwel Basin, Kenya. This depended on finding a relationship between daily rainfall and Normalized Difference Water Index (NDWI). Among multiple rainfall products evaluated, Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) was selected due to its fine spatial resolution and performance. Daily NDWI time series derived from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) imagery were used as a proxy for water accumulation and flood indicators. A python-based Decision Tree Regressor (DTR) model was trained using the daily CHIRPS rainfall data with various lag times, along with auxiliary meteorological variables including relative humidity, wind speed, and mean temperature for the period from 2002 to 2024 to predict NDWI of Lodwar Town. The machine learning model substantially improved the correlation between rainfall and NDWI, raising the correlation coefficient by 25%. Spatial analysis of rainfall-NDWI correlation revealed that areas in the west, northwest, and southwest of Lodwar Town, with elevations between 508 m and 648 m have high correlation. Rainfall in these regions can serve as signal for potential rapid flooding with 0-day lag-time in Lodwar Town situated at an elevation of approximately 500 m. These areas are not necessarily the primary high rainfall sources, rather they act as signal zones for floods of Lodwar Town that can provide flood early warning information. The proposed methodology in this study can offer a practical approach to anticipatory action and flood risk reduction for vulnerable communities in remote regions with no or limited hydrometeorological stations. |
| format | Journal Article |
| id | CGSpace177234 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1772342025-12-08T10:29:22Z Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya Lakew, Haileyesus Belay Taye, Meron Teferi Lino, O. Dyer, E. flood forecasting remote sensing machine learning rainfall models Reliable flood monitoring and prediction remain a challenge in data-scarce regions, particularly in arid and semi-arid environments. This study explores the integration of remote sensing data and machine learning techniques to improve flood detection and early warning capabilities in Lodwar Town of the Turkwel Basin, Kenya. This depended on finding a relationship between daily rainfall and Normalized Difference Water Index (NDWI). Among multiple rainfall products evaluated, Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) was selected due to its fine spatial resolution and performance. Daily NDWI time series derived from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) imagery were used as a proxy for water accumulation and flood indicators. A python-based Decision Tree Regressor (DTR) model was trained using the daily CHIRPS rainfall data with various lag times, along with auxiliary meteorological variables including relative humidity, wind speed, and mean temperature for the period from 2002 to 2024 to predict NDWI of Lodwar Town. The machine learning model substantially improved the correlation between rainfall and NDWI, raising the correlation coefficient by 25%. Spatial analysis of rainfall-NDWI correlation revealed that areas in the west, northwest, and southwest of Lodwar Town, with elevations between 508 m and 648 m have high correlation. Rainfall in these regions can serve as signal for potential rapid flooding with 0-day lag-time in Lodwar Town situated at an elevation of approximately 500 m. These areas are not necessarily the primary high rainfall sources, rather they act as signal zones for floods of Lodwar Town that can provide flood early warning information. The proposed methodology in this study can offer a practical approach to anticipatory action and flood risk reduction for vulnerable communities in remote regions with no or limited hydrometeorological stations. 2025-10-21 2025-10-21T10:13:14Z 2025-10-21T10:13:14Z Journal Article https://hdl.handle.net/10568/177234 en Open Access Frontiers Media Lakew, H. B.; Taye, M. T.; Lino, O.; Dyer, E. 2025. Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya. Frontiers in Water, 7:1683545. doi: https://doi.org/10.3389/frwa.2025.1683545 |
| spellingShingle | flood forecasting remote sensing machine learning rainfall models Lakew, Haileyesus Belay Taye, Meron Teferi Lino, O. Dyer, E. Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya |
| title | Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya |
| title_full | Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya |
| title_fullStr | Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya |
| title_full_unstemmed | Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya |
| title_short | Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya |
| title_sort | remote sensing and machine learning integration to detect and forecast floods in lodwar town turkwel basin kenya |
| topic | flood forecasting remote sensing machine learning rainfall models |
| url | https://hdl.handle.net/10568/177234 |
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