"background": "The expansion of water treatment infrastructure is critical for public health and development. In Ethiopia, significant investment has been made, yet a robust methodological framework for evaluating system performance and forecasting future adoption is lacking, hindering strategic planning. ", "purpose and objectives": "This study aims to develop and validate a methodological framework for evaluating water treatment systems and to construct a time-series forecasting model for national adoption rates, providing a tool for evidence-based infrastructure planning. ", "methodology": "A longitudinal dataset of national system commissioning was analysed. Methodological evaluation was conducted via multi-criteria analysis. Forecasting employed an Autoregressive Integrated Moving Average (ARIMA) model, specified as \ yt = \1 \ y{t-1 + \1 -1 + \, where parameters were estimated using maximum likelihood. Model diagnostics included analysis of residual autocorrelation. ", "findings": "The methodological evaluation revealed systemic gaps in operational monitoring protocols. The ARIMA (1, 1, 1) model provided the best fit, forecasting a continued positive trajectory in adoption rates with a projected increase of approximately 22% over the forecast horizon. Forecast uncertainty, represented by 95% confidence intervals, widened notably in the later stages of the projection. ", "conclusion": "The developed framework offers a replicable method for evaluating water treatment infrastructure, while the forecasting model provides a statistically sound basis for predicting adoption trends, essential for resource allocation and policy development. ", "recommendations": "Implement the proposed evaluation framework for ongoing projects. Integrate the forecasting model into the national infrastructure planning cycle, with periodic recalibration using new data to maintain accuracy. ", "key words": "Infrastructure planning, time-series analysis, ARIMA modelling, water treatment, systems evaluation, forecasting", "contribution statement": "This paper provides a novel integrated methodology combining system evaluation with statistical forecasting, specifically developed for water infrastructure in a developing context, and yields the first publicly available long-term forecast model for water
Meklit Abebe (Sat,) studied this question.