Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya

Aug 5, 2026

Reliable flood monitoring and prediction remain a major challenge in data-scarce arid and semi-arid regions. This study integrates remote sensing and machine learning to enhance flood detection and early warning for Lodwar Town in Kenya’s Turkwel Basin. Daily rainfall (CHIRPS) and Aqua MODIS NDWI are linked to model water accumulation using a Python Decision Tree Regressor trained with lagged rainfall and meteorological variables from 2002–2024, improving rainfall–NDWI correlation by 25% and identifying west–northwest high-signal zones with actionable 0-day response.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Study area and flood context

Data sources and rainfall products

NDWI extraction from remote sensing imagery

Machine learning model setup

Results and spatial correlation analysis

Implications for early warning and anticipatory action

OPEN ACCESS EDITED BY Alexandra Gemitzi, Democritus University of Thrace, Greece REVIEWED BY Indra Mani Tripathi, Indian Institute of Technology Gandhinagar, India Luh Joni Erawati Dewi, Ganesha University of Education, Indonesia *CORRESPONDENCE Meron Teferi Taye [meron.taye@cgiar.org](meron.taye@cgiar.org) RECEIVED 11 August 2025 ACCEPTED 03 October 2025 PUBLISHED 21 October 2025 CITATION Lakew HB, Taye MT, Lino O and Dyer E (2025) Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya. Front. Water 7:1683545 . doi: 10.3389/frwa.2025.1683545 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) and the copyright owner(s) 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. TYPE Original Research PUBLISHED 21 October 2025 DOI 10.3389/frwa.2025.1683545 Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya Haileyesus Belay Lakew 1,2, Meron Teferi Taye 1*, Oscar Lino3 and Ellen Dyer4 1 International Water Management Institute, Addis Ababa, Ethiopia, 2Center for Water Research, Institute of Water, Environment and Climate Research (IWECR), Addis Ababa University, Addis Ababa, Ethiopia, 3 Department of Meteorology, University of Nairobi, Nairobi, Kenya, 4School of Geography and the Environment, University of Oxford, Oxford, United Kingdom 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 1 Introduction Floods have become a pressing global issue, causing widespread devastation and economic losses. Globally, during 1990–2022 period 4,713 events were recorded i

FAQs

Why are flood monitoring and prediction difficult in the study region?

The document states that reliable flood monitoring and prediction remain challenging in data-scarce arid and semi-arid environments where hydrometeorological stations may be limited.

Which remote sensing and rainfall data are used to build the flood detection approach?

It uses daily NDWI time series derived from Aqua MODIS imagery as a water accumulation proxy, and CHIRPS daily rainfall data because it offers fine spatial resolution and good performance among evaluated products.

How does the decision tree model improve flood-related relationships?

A Python Decision Tree Regressor is trained with lagged CHIRPS rainfall and auxiliary meteorological variables (e.g., humidity, wind speed, mean temperature) to predict NDWI, substantially increasing the rainfall–NDWI correlation coefficient by 25%.

Aug 5, 2026

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