This study aims to evaluate regional monitoring networks in Ethiopia's systems by applying a Bayesian hierarchical model for assessing system reliability. A Bayesian hierarchical model is employed to analyse data from multiple monitoring stations distributed throughout Ethiopia's diverse geographical regions. This approach allows for the integration of local and global information, enabling more accurate predictions of system reliability under varying conditions. The analysis reveals significant spatial variations in system performance across different regions, with some areas showing a 20% higher likelihood of maintaining optimal monitoring accuracy compared to others. These findings highlight the importance of localized data collection in enhancing system reliability. The Bayesian hierarchical model demonstrates its effectiveness in assessing and improving regional monitoring networks' reliability in Ethiopia. This study contributes new insights into how spatial variability can be incorporated into reliability assessment models, offering a practical tool for policymakers and practitioners. Based on the findings, it is recommended that future studies focus on developing integrated regional monitoring strategies that consider both local conditions and broader system performance metrics. Additionally, continuous data validation and model updates are essential to maintain optimal reliability levels across all regions. Bayesian hierarchical models, Regional monitoring networks, System reliability, Ethiopia The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Girma et al. (Fri,) studied this question.