A Bayesian Hierarchical Model for Risk Reduction in Ethiopian Water Treatment Systems: A Methodological Evaluation
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2014
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| _version_ | 1866901387938365440 |
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| author | Girma, Selamawit Assefa, Tewodros Abebe, Meklit |
| author_facet | Girma, Selamawit Assefa, Tewodros Abebe, Meklit |
| contents | <p>{ "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 $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\tau^2)$, where $y{ij}$ is the risk metric for facility $i$ in region $j$, $\\alphaj$ are region-specific intercepts, and $X{ij}$ 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</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18971155 |
| institution | Zenodo |
| language | eng |
| publishDate | 2014 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Bayesian Hierarchical Model for Risk Reduction in Ethiopian Water Treatment Systems: A Methodological Evaluation Girma, Selamawit Assefa, Tewodros Abebe, Meklit Bayesian hierarchical modelling risk assessment water treatment systems Sub-Saharan Africa operational reliability public health engineering methodological evaluation <p>{ "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 $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\tau^2)$, where $y{ij}$ is the risk metric for facility $i$ in region $j$, $\\alphaj$ are region-specific intercepts, and $X{ij}$ 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</p> |
| title | A Bayesian Hierarchical Model for Risk Reduction in Ethiopian Water Treatment Systems: A Methodological Evaluation |
| topic | Bayesian hierarchical modelling risk assessment water treatment systems Sub-Saharan Africa operational reliability public health engineering methodological evaluation |
| url | https://doi.org/10.5281/zenodo.18971155 |