aceA Review of Water Quality Forecasting and Classification Using Machine Learning Models and Statistical Analysis
The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy an…
Hybrid machine learning models that integrate multiple approaches improved predictive accuracy and robustness for forecasting and classifying river water quality to support sustainable water resources management.
Machine learning models for water quality forecasting still face persistent challenges in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques.
Challenges remain in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques, limiting transparent and adaptive deployment.
Evidence
- Peer-reviewedWater2025-07-28
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Truvace Impact Record TRV-2026-0497, v1: “A Review of Water Quality Forecasting and Classification Using Machine Learning Models and Statistical Analysis.” Truvace, 2026-07-22. /record/TRV-2026-0497 (accessed at citation time). sha256 2d491ca609bb7134…
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