{ "background": "The long-term financial sustainability of water treatment infrastructure in developing nations is a critical engineering challenge. Existing diagnostic tools often lack the temporal resolution to forecast cost-effectiveness, hindering proactive asset management and capital planning. ", "purpose and objectives": "This study develops and validates a novel time-series forecasting model to diagnose the cost-effectiveness of water treatment facilities. The primary objective is to provide a predictive tool for infrastructure performance and unit cost trends to inform maintenance and investment decisions. ", "methodology": "A longitudinal dataset of operational and financial parameters from multiple facilities was analysed. The core methodology employs a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model, specified as \ (B) \ (Bˢ) \ᵈ\D yt = \ (B) \ (Bˢ) \ + \ Xt, where Xₜ includes capacity utilisation and energy cost indices. Model parameters were estimated using maximum likelihood. ", "findings": "The model forecasts a significant upward trend in real unit treatment costs, with a predicted mean increase of 17. 3% over the forecast horizon (95% prediction interval: 12. 1% to 22. 8%). Diagnostic analysis identified energy consumption and chemical dosing efficiency as the most sensitive determinants of future cost-effectiveness. ", "conclusion": "The proposed time-series model provides a robust, evidence-based diagnostic framework for forecasting infrastructure cost trajectories. It confirms that without targeted interventions, operational expenditures will escalate substantially, threatening economic sustainability. ", "recommendations": "Infrastructure managers should adopt predictive, model-informed diagnostics for routine asset management. Capital investment should prioritise retrofits that improve energy and chemical efficiency, as these levers most directly influence long-term cost curves. ", "key words": "infrastructure diagnostics, time-series analysis, cost forecasting, water treatment, asset management, SARIMAX", "contribution statement": "This paper presents a novel application of SARIMAX modelling for the predictive cost-diagnosis of water treatment infrastructure
Uwimana et al. (Sat,) studied this question.