Bayesian Hierarchical Model Evaluation for Yield Improvement in South African Water Treatment Facilities Systems

Water treatment facilities in South Africa face challenges related to yield variability due to varying inputs and operational conditions. A Bayesian hierarchical model was applied to historical data from multiple water treatment plants. The model incorporates spatial and temporal dependencies using Markov Chain Monte Carlo (MCMC) methods with robust standard errors. The model demonstrated a significant reduction in prediction uncertainty, specifically reducing the coefficient of variation for yield predictions by approximately 20% across all facilities studied. The Bayesian hierarchical model provided more precise yield estimates compared to traditional statistical models, highlighting its utility for improving operational efficiency and resource allocation in South African water treatment systems. Implementation of this model should be considered as a best practice for enhancing the performance and reliability of water treatment facilities in South Africa. Bayesian Hierarchical Model, Yield Improvement, Water Treatment Facilities, South Africa The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.