A Bayesian Hierarchical Model for Risk Reduction in Senegal's Power Distribution Network: A Methodological Evaluation, 2000–2026

{ "background": "Power distribution infrastructure in many developing nations faces significant reliability challenges due to ageing assets, environmental stressors, and limited maintenance data. Quantitative frameworks for prioritising risk mitigation investments in such data-scarce environments are critically needed. ", "purpose and objectives": "This study presents a methodological evaluation of a novel Bayesian hierarchical model designed to quantify risk reduction for electrical distribution equipment. The objective is to provide a robust, probabilistic tool for infrastructure investment planning under uncertainty. ", "methodology": "A Bayesian hierarchical model was developed, integrating sparse failure records, expert elicitation, and covariates including equipment age and environmental exposure. The core model structure is \ (\, \), where is the failure rate for equipment type i in region j, with hyperparameters informed by regional operational conditions. Model performance was evaluated using posterior predictive checks and cross-validation. ", "findings": "The model successfully identified high-risk equipment categories, with underground cables in coastal regions showing a posterior probability of 0. 87 of exceeding a critical failure threshold. Predictive intervals for failure rates were substantially narrowed by the hierarchical borrowing of strength, reducing the average credible interval width by approximately 40% compared to non-hierarchical models. ", "conclusion": "The Bayesian hierarchical approach provides a statistically rigorous and operationally actionable framework for risk assessment in power distribution networks characterised by data limitations. It effectively synthesises disparate information sources to guide infrastructure resilience planning. ", "recommendations": "Infrastructure planners should adopt probabilistic risk models that formally account for parameter uncertainty. Future work should integrate real-time sensor data to dynamically update the model's failure rate estimates. ", "key words": "Bayesian statistics, infrastructure resilience, power distribution, risk assessment, hierarchical modelling, predictive maintenance", "contribution statement": "This paper introduces a novel application of Bayesian hierarchical modelling to power distribution risk in a data-scarce context, demonstrating a method to rigorously quantify uncertainty in asset failure rates for