"background": "Forecasting the yield of water treatment facilities is critical for infrastructure planning and resource management in developing nations. Existing models often lack the methodological rigour to account for the specific operational and climatic variabilities encountered in such contexts, leading to unreliable projections. ", "purpose and objectives": "This article presents a novel methodological framework for generating robust, long-term forecasts of water treatment yield. The primary objective is to provide a replicable procedure for evaluating system performance and measuring potential yield improvement. ", "methodology": "The framework integrates an autoregressive integrated moving average with exogenous variables (ARIMAX) model, formalised as Yt = \ + =1^{p\ Yt-i + =1^q\ -j + =1^r\ Xt, k + \, where Xt represents exogenous climatic and operational covariates. Model parameters are estimated using maximum likelihood, with inference on forecast uncertainty provided via 95% prediction intervals derived from robust standard errors. ", "findings": "The methodological application demonstrates a projected upward trend in potential yield, with a mean forecast increase of approximately 18% over the forecast horizon. The prediction intervals widen notably after the fifth forecast period, highlighting increased uncertainty in longer-term projections. ", "conclusion": "The proposed ARIMAX-based framework provides a statistically sound and operationally relevant methodology for forecasting water treatment yield, directly addressing gaps in context-specific modelling. ", "recommendations": "It is recommended that engineering planners adopt this framework for baseline assessments and integrate real-time data to periodically recalibrate forecasts. Further research should focus on incorporating non-linear covariates and machine learning ensembles. ", "key words": "water treatment yield, time-series forecasting, ARIMAX, infrastructure planning, methodological framework", "contribution statement": "This paper contributes a novel, statistically robust methodological framework specifically designed for forecasting water treatment yield in data-scarce environments
Tesfaye et al. (Fri,) studied this question.