Water quality estimation and algae bloom prediction using machine learning – A case study

doi:10.1016/j.jwpe.2025.109161

Water quality estimation and algae bloom prediction using machine learning – A case study

2025-11-24

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DOI：10.1016/j.jwpe.2025.109161

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• Cropland nutrient runoff contributed to eutrophication by increasing aquatic chlorophyll levels and algae proliferation. • Random Forest outperformed other machine learning models used in water quality estimation. • Random Forest achieved the highest accuracy in predicting chlorophyll concentration and algae density. • AI-driven monitoring and sustainable nutrient management are essential for mitigating eutrophication and safeguarding freshwater ecosystems.

Water quality estimation and algae bloom prediction using machine learning – A case study

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