Water treatment facilities in Tanzania face challenges related to water quality variability, leading to frequent system downtime and increased operational costs. A comprehensive analysis was conducted using a seasonal autoregressive integrated moving average (SARIMA) model to forecast water quality parameters over a five-year period. Robust uncertainty intervals were calculated to assess the reliability of the forecasts. The SARIMA model demonstrated an R² value of 0. 85, indicating that the model accurately predicted water quality trends with moderate precision. The findings suggest that time-series analysis can be effectively utilised for risk management in water treatment facilities, providing a robust framework to anticipate and mitigate potential disruptions. Investment should focus on enhancing maintenance protocols based on forecasted data and ensuring regular system calibration to maintain optimal performance levels. Water Treatment Facilities, Time-Series Analysis, Risk Reduction, SARIMA Model, Uncertainty Intervals The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Mwalimu et al. (Thu,) studied this question.