"background": "Water treatment systems in Ethiopia face significant operational challenges, leading to variable performance and public health risks. Current risk assessment methods often lack the capacity to integrate sparse, multi-level data and quantify uncertainty for infrastructure management decisions. ", "purpose and objectives": "This study aimed to develop and evaluate a novel Bayesian hierarchical modelling framework for the methodological assessment of risk reduction in water treatment facilities. The objective was to provide a robust tool for quantifying performance improvements and associated uncertainties. ", "methodology": "A Bayesian hierarchical model was constructed, integrating facility-level operational data with regional environmental covariates. The core model structure is y{ij \ (\ + \ Xij, \²), \ \ (\\, \²), where yij is the risk metric for facility i in region j, \ are region-specific intercepts, and Xij are covariates. Model inference used Hamiltonian Monte Carlo sampling. ", "findings": "The model demonstrated a high predictive capacity for system failure risk, with posterior credible intervals for key performance coefficients excluding zero. A principal finding was that improved coagulation control was associated with a median estimated 34% reduction in turbidity-related risk across the evaluated facilities. Uncertainty was successfully partitioned into facility and regional components. ", "conclusion": "The proposed Bayesian hierarchical model provides a statistically rigorous methodology for evaluating risk reduction in complex water treatment systems. It effectively synthesises heterogeneous data and quantifies uncertainty, offering a superior alternative to conventional deterministic assessments. ", "recommendations": "Adoption of this modelling framework is recommended for asset management planning by water authorities. Future work should focus on integrating real-time sensor data to enable dynamic risk forecasting. ", "key words": "Bayesian inference, hierarchical modelling, risk assessment, water treatment, infrastructure reliability, uncertainty quantification", "contribution statement": "This paper presents a novel probabilistic framework for infrastructure risk assessment
Girma et al. (Mon,) studied this question.