A Bayesian Predictive Framework for Early Detection of Water Quality Degradation in Urban Distribution Network

Abstract

In recent times, the scenarios where the modern quality monitoring systems are used have been changing rapidly, with the quality being changed over time, the situation being uncertain and the amount of data being extremely limited, thus fundamentally challenging the assumptions of the classical Statistical Process Control (SPC) methods. This article proposes quality monitoring within the Bayesian framework and Predictive Posterior Error (PPE), which facilitates shifting the emphasis in quality control from the gathering of historical data to the delivering of forward, looking predictive evaluations. The framework integrates latent regime, switching dynamics with robust observation models to explain structural changes, heavy, tailed noise, and low event, rate processes. Control charts based on PPE are formed from the posterior predictive mean, which results in monitoring signals that are interpretable and probabilistically calibrated. The proposed approach reliably controls the false alarm rate and detects the changes effectively in both stationary and non, stationary situations as demonstrated by comprehensive simulation studies, including outlier and intentional model misspecification cases. Further findings reveal favourable accuracy, efficiency trade, offs among the different exact and approximate Bayesian inference regimes. The paper presents a practical data application that demonstrates the efficacy of the developed tool, where PPE, based alarms correspond closely to the known process insults but at the same time these alarms are smoother and earlier than the traditional charts. In summary, the outcomes denote the posterior predictive expectation as a lasagna, laying and operationally relevant concept for modern Bayesian quality surveillance systems.