Statistical Modelling of Wastewater Quality Prediction using AI and IOT Sensors

Rapid urban expansion continues to intensify pressure on wastewater networks, increasing the need for reliable systems capable of real-time effluent condition forecasting. Traditional laboratory procedures, though accurate, cannot meet the speed, scalability, and operational responsiveness required for modern water governance. This study introduces a streamlined yet robust framework that integrates statistical modelling with intelligent sensing and analytical systems to deliver fast, interpretable, and high-precision assessments of effluent behavior. Continuous measurements of pH, turbidity, dissolved oxygen, conductivity, and biochemical oxygen demand were acquired from distributed smart sensors to capture dynamic variations in effluent characteristics. Statistical methods-including regression analysis, multivariate modelling, and Principal Component Analysis-were used to isolate key predictors, reduce dimensionality, and expose hidden data structures. Statistical Process Control charts enabled immediate recognition of deviations from operational thresholds, ensuring early detection of abnormal conditions. These statistical insights were further strengthened using advanced learning models such as Random Forest, Support Vector Regression, and Long ShortTerm Memory networks, which effectively captured non-linear responses and temporal dependencies. The integrated system demonstrated substantial performance gains, reducing forecasting errors by nearly 30% and consistently achieving high predictive accuracy (R ² > 0.90) even under noisy or partial data streams. Anomaly detection improved by approximately 20%, confirming the system's resilience and suitability for field environments. The core novelty lies in the unified combination of statistical inference, machine-learning-based forecasting, and real-time sensor intelligence within a single operational architecture. This approach enhances proactive effluent management and supports sustainable, regulation-compliant water operations.