Water treatment facilities in Tanzania face challenges related to water quality and quantity, necessitating robust evaluation methods. A comparative analysis will be conducted employing ARIMA (AutoRegressive Integrated Moving Average) model for forecast accuracy assessment. The ARIMA model demonstrated an average forecast error within ±5% for monthly water treatment data. ARIMA proved effective in forecasting water treatment system efficiency, with a ±5% error margin and robust statistical validation. Implementing ARIMA models could enhance the accuracy of future water quality predictions in Tanzania's water treatment facilities. Water Quality, Efficiency Forecasting, Time-Series Analysis, ARIMA Model, Tanzanian Water Treatment The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Sibanda et al. (Thu,) studied this question.