ArticleScientific reports2025
Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Flood susceptibility assessment using three machine learning techniques and comparison of their performance.Scientific reports · 2026Article
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2 authors.
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Abstract
Monitoring the temporal evolution of surface water distribution is crucial to support surface water management and conservation, and could also effectively contribute to the achievement of Sustainable Development Goal 6 (SDG 6) 'Clean Water and Sanitation' at the regional level. Despite its importance, there is a lack of an operational method for determining surface water extent that also shows the interannual variability in Ethiopia. We assess Gradient Tree Boosting (GTB), Support Vector Machines (SVM), and Random Forest (RF) running on the Google Earth Engine (GEE) using Landsat for surface water monitoring at four sites in Ethiopia from 1986 to 2023. The results show that GTB, RF, and SVM have excellent classification accuracies, with overall, producer, and user accuracies consistently above 90%. GTB slightly outperforms the other two machine learning methods. The estimated water cover for our study sites shows a high degree of agreement with a benchmark dataset from the Joint Research Center (JRC), as indicated by coefficient of determination (R
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