"background": "Evaluating the long-term cost-effectiveness of water treatment systems is critical for sustainable infrastructure management in developing regions. Existing diagnostic methods often lack the temporal granularity and predictive capability required for proactive maintenance and capital planning. ", "purpose and objectives": "This study aimed to develop and validate a novel time-series forecasting model to diagnose and predict the cost-effectiveness of water treatment facilities, providing a tool for forward-looking asset management. ", "methodology": "A methodological framework was constructed using historical operational and financial data from multiple facilities. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as \ yt = \ + =1^{p\ \ yt-i + =1^q\ -i + =1^k\ Xj, t + \ₜ, where cost-effectiveness is the dependent variable. Model parameters were estimated using maximum likelihood, and robust standard errors were calculated to ensure inference reliability. ", "findings": "The model demonstrated strong predictive accuracy, with a mean absolute percentage error (MAPE) of 8. 7% for out-of-sample forecasts. A key diagnostic output indicated a persistent downward trend in the cost-effectiveness ratio for conventional coagulation-filtration plants, signalling a mean annual efficiency degradation of approximately 2. 3%. ", "conclusion": "The developed time-series model provides a robust, quantitative tool for diagnosing historical performance and forecasting future cost-effectiveness of water treatment infrastructure, enabling data-driven decision-making. ", "recommendations": "Infrastructure managers should adopt similar forecasting diagnostics for routine system evaluation. Future research should integrate real-time sensor data to enhance model responsiveness and explore regional scalability. ", "key words": "infrastructure asset management, predictive maintenance, ARIMAX modelling, water utility finance, operational efficiency", "contribution statement": "This paper presents a novel application of ARIMAX time-series forecasting for the
Mtewele et al. (Fri,) studied this question.