Advanced machine learning models for accurate water quality classification and WQI prediction: Implications for aquatic disease risk management
Md. Abdullah Al Mamun Hridoy, Abdullah Ibna Shawkat, Chiara Bordin, Mahima Ranjan Acharjee, A. Masood, Azeez Olalekan Baki, Md Abdullah Al Mamun
Abstract
of 0.9685. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions and quantify feature contributions. Dissolved oxygen and BOD emerged as the most influential predictors, followed by turbidity, nitrate, and electrical conductivity, aligning with known risk factors for aquatic disease outbreaks. These findings underscore the potential of combining advanced machine learning with explainable AI techniques and real-time water quality data to enable proactive monitoring and early warning systems for sustainable aquatic health management.
Topics & Concepts
Water qualityMachine learningSupport vector machineSafeguardingArtificial intelligenceEnsemble learningArtificial neural networkComputer scienceQuality (philosophy)Risk assessmentAquatic ecosystemPredictive modellingRandom forestWarning systemFeature (linguistics)Decision support systemRisk managementData miningBiochemical oxygen demandGradient boostingStatistical classificationMultivariate statisticsWater resourcesEarly warning systemHyperparameter optimizationRisk analysis (engineering)Decision treeHydrological Forecasting Using AIWater Quality Monitoring TechnologiesWater Quality and Pollution Assessment