Bridging the Gap: A Review of Machine Learning in Water Quality Control
Keywords:
Hybrid system, Machine Learning, Predictive model, Water contaminants, Water quality monitoringAbstract
Water quality management faces escalating global challenges due to pollution, climate change, and population growth. This review critically examines the integration of machine learning (ML) with conventional water quality monitoring and treatment methods, presenting a systematic comparison of their capabilities, limitations, and synergies. While traditional techniques like atomic absorption spectroscopy (AAS) and chromatography provide unmatched precision (e.g., detecting arsenic at 0.1 ppb), they suffer from high costs ($200/sample), latency (24-72 hours), and scalability barriers. ML-driven solutions, including LSTM networks and random forest models, enable real-time anomaly detection (e.g., 85% accurate algal bloom prediction 7 days in advance) and operational optimization (15% cost reduction in wastewater treatment). Hybrid frameworks such as sensor fusion systems (reducing measurement errors by 83%) and digital twins (preventing 60% of public health risks during contamination events) demonstrate transformative potential by bridging these approaches. However, persistent challenges include data scarcity in developing regions (only 12% of Sub-Saharan African monitoring stations provide real-time data), algorithmic bias (65% sensitivity loss in cross-regional models), and regulatory skepticism toward "black-box" systems. Emerging solutions like federated learning (35% accuracy improvement in pan-African E. coli prediction) and explainable AI (SHAP-guided nitrate models) address these barriers. Future directions explore IoT-edge systems (90% accurate TinyML sensors at 50 mW power), quantum-optimized adsorbents (2.5x mercury removal efficiency), and satellite-enabled global monitoring (85% microplastic detection accuracy). The review underscores the necessity of interdisciplinary collaboration to standardize hybrid frameworks, ensure equitable data governance, and translate technological innovations into actionable policies for sustainable water security.References
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