Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period
Municipal water managers rely on pipe deterioration models to plan maintenance, repair, and replacement. Although efforts have been made to increase their accuracy, these models are subject to uncertainties in the predictions. In this paper, an optimization procedure of the Bayesian updating period...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-01-01
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| Series: | Journal of Hydroinformatics |
| Subjects: | |
| Online Access: | http://jhydro.iwaponline.com/content/25/1/1 |
| _version_ | 1797200728888967168 |
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| author | Ngandu Balekelayi Solomon Tesfamariam |
| author_facet | Ngandu Balekelayi Solomon Tesfamariam |
| author_sort | Ngandu Balekelayi |
| collection | DOAJ |
| description | Municipal water managers rely on pipe deterioration models to plan maintenance, repair, and replacement. Although efforts have been made to increase their accuracy, these models are subject to uncertainties in the predictions. In this paper, an optimization procedure of the Bayesian updating period of the parameters of an existing deterioration model is proposed to sequentially reduce the uncertainty in the prediction of the water pipe breakage rate variable. This latter is modeled using a structured geoadditive regression technique where covariates are allowed to have linear (e.g., categorical) and nonlinear (e.g., continuous) relationships with the response variable. Unknown and unobserved covariates are included in the model through a geospatial component that captures spatial auto-correlations and local heterogeneities. The optimization procedure searches through the time series data to identify the optimal updating period horizon that corresponds to the minimum error between the predicted coefficient of determination between predictions and observations using the unupdated and updated models. The process is repeated until the entire time series data is covered. The application of this approach to failure data of large Canadian urban water systems shows a significant reduction in the uncertainty of the parameters and increases the accuracy in the prediction of the output response variable.
HIGHLIGHTS
Optimization.;
Bayesian Updating.;
Gaussian Markov Random Fields.;
P-Splines.;
Deterioration model.; |
| first_indexed | 2024-04-10T09:33:03Z |
| format | Article |
| id | doaj.art-61edd5c36c8d4af5b322c3288371a5e7 |
| institution | Directory Open Access Journal |
| issn | 1464-7141 1465-1734 |
| language | English |
| last_indexed | 2024-04-24T07:36:16Z |
| publishDate | 2023-01-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Hydroinformatics |
| spelling | doaj.art-61edd5c36c8d4af5b322c3288371a5e72024-04-20T06:17:41ZengIWA PublishingJournal of Hydroinformatics1464-71411465-17342023-01-0125111910.2166/hydro.2022.040040Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating periodNgandu Balekelayi0Solomon Tesfamariam1 School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada Municipal water managers rely on pipe deterioration models to plan maintenance, repair, and replacement. Although efforts have been made to increase their accuracy, these models are subject to uncertainties in the predictions. In this paper, an optimization procedure of the Bayesian updating period of the parameters of an existing deterioration model is proposed to sequentially reduce the uncertainty in the prediction of the water pipe breakage rate variable. This latter is modeled using a structured geoadditive regression technique where covariates are allowed to have linear (e.g., categorical) and nonlinear (e.g., continuous) relationships with the response variable. Unknown and unobserved covariates are included in the model through a geospatial component that captures spatial auto-correlations and local heterogeneities. The optimization procedure searches through the time series data to identify the optimal updating period horizon that corresponds to the minimum error between the predicted coefficient of determination between predictions and observations using the unupdated and updated models. The process is repeated until the entire time series data is covered. The application of this approach to failure data of large Canadian urban water systems shows a significant reduction in the uncertainty of the parameters and increases the accuracy in the prediction of the output response variable. HIGHLIGHTS Optimization.; Bayesian Updating.; Gaussian Markov Random Fields.; P-Splines.; Deterioration model.;http://jhydro.iwaponline.com/content/25/1/1degradationfailure predictioninferenceoptimalspatial correlationurban water system |
| spellingShingle | Ngandu Balekelayi Solomon Tesfamariam Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period Journal of Hydroinformatics degradation failure prediction inference optimal spatial correlation urban water system |
| title | Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period |
| title_full | Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period |
| title_fullStr | Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period |
| title_full_unstemmed | Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period |
| title_short | Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period |
| title_sort | bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period |
| topic | degradation failure prediction inference optimal spatial correlation urban water system |
| url | http://jhydro.iwaponline.com/content/25/1/1 |
| work_keys_str_mv | AT ngandubalekelayi bayesiangeoadditivewaterpipefailureforecastingmodelbyoptimizingtheupdatingperiod AT solomontesfamariam bayesiangeoadditivewaterpipefailureforecastingmodelbyoptimizingtheupdatingperiod |