"background": "Water treatment infrastructure in many developing nations faces persistent challenges in reliability and risk management. Current diagnostic approaches often lack a formal framework to quantify uncertainty and integrate sparse, multi-level operational data, hindering targeted maintenance and investment. ", "purpose and objectives": "This study develops and validates a novel Bayesian hierarchical model to diagnose and quantify risk reduction in water treatment systems. The objective is to provide a robust probabilistic tool for infrastructure managers to prioritise interventions based on system-specific failure likelihoods. ", "methodology": "A hierarchical model was constructed, y{ij \ (), \\; () = + \ Xij, where yij is the failure status for component i in plant j, \ⱼ are plant-level random effects, and X are covariates. The model was applied to operational data from 27 treatment facilities, using Hamiltonian Monte Carlo for inference. ", "findings": "Posterior distributions indicated that enhanced chemical dosing protocols reduced the median probability of critical filtration failure by 34% (95% Credible Interval: 28% to 39%). The model successfully identified three specific plant clusters where infrastructural age was the dominant risk factor, overshadowing other operational variables. ", "conclusion": "The Bayesian hierarchical model provides a statistically rigorous diagnostic framework, explicitly quantifying uncertainty in risk estimates for complex water treatment systems. It moves beyond deterministic assessments to support evidence-based decision-making. ", "recommendations": "Infrastructure agencies should adopt probabilistic risk diagnostics to allocate resources. Future model extensions should incorporate real-time sensor data to enable dynamic risk forecasting. ", "key words": "Bayesian inference, infrastructure risk, probabilistic modelling, water treatment, maintenance prioritisation", "contribution statement": "This paper presents a novel application of Bayesian hierarchical modelling to the diagnostic evaluation of water treatment systems, providing a new method to quantify risk reduction with explicit uncertainty
Ankrah et al. (Fri,) studied this question.