ORIGINAL RESEARCH article
Front. Water
Sec. Water and Climate
Volume 7 - 2025 | doi: 10.3389/frwa.2025.1683545
Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel basin, Kenya
Provisionally accepted
- 1International Water Management Institute (Ethiopia), Addis Ababa, Ethiopia
- 2University of Nairobi, Nairobi, Kenya
- 3University of Oxford, Oxford, United Kingdom
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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.
Keywords: flood, machine learning, Decision tree regression, remote sensing, Lodwar Town
Received: 11 Aug 2025; Accepted: 03 Oct 2025.
Copyright: © 2025 Lakew, Taye, Lino and Dyer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Meron Teferi Taye, meron.taye@cgiar.org
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