A Time-Series Forecasting Model for Yield Improvement in Kenyan Water Treatment Systems: A Methodological Evaluation, 2000–2026

{ "background": "Persistent inefficiencies in water treatment yield within the country's infrastructure hinder reliable water supply. Existing operational models often lack robust predictive capabilities for long-term performance improvement, necessitating advanced analytical frameworks.", "purpose and objectives": "This working paper presents a methodological evaluation of a novel time-series forecasting model designed to measure and predict yield improvement in water treatment systems. The objective is to assess the model's technical validity and applicability for infrastructure planning.", "methodology": "The methodology employs an Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) model, specified as \Δ Yt = \α + \∑i=1ᵖ\ \Δ Yt-i + \∑j=1q\ \εt-j + \∑k=1ʳ\ Xk,t + \, where $Yt$ is treatment yield. Model parameters were estimated using maximum likelihood, with inference based on heteroskedasticity-robust standard errors. Evaluation utilised historical operational data from multiple facilities.", "findings": "The model demonstrates a statistically significant positive trend in potential yield improvement, with a forecasted average increase of 12–18% over the projection period under optimal conditions. Parameter estimates for key maintenance and chemical dosing variables were significant at the 95% confidence level, indicating their strong predictive utility.", "conclusion": "The ARIMAX framework provides a technically sound and operationally relevant method for forecasting yield improvements, offering a superior alternative to descriptive benchmarks for strategic asset management.", "recommendations": "Implement the model as a decision-support tool for capital investment prioritisation and operational budgeting. Future work should integrate real-time sensor data to transition from periodic to continuous forecasting.", "key words": "water treatment yield, time-series forecasting, ARIMAX, infrastructure performance, asset management", "contribution statement": "This paper provides a novel, rigorously evaluated forecasting methodology that enables quantitative, evidence