Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period. Issue 1 (3rd December 2022)

• Record Type: Journal Article
• Authors: Balekelayi, Ngandu; Tesfamariam, Solomon
• Is Part Of: Journal of hydroinformatics. Volume 25:Issue 1(2023)
• Page Start: 1
• Page End: 19
• Publication Date: 2022-12-03
• Subjects: degradation -- failure prediction -- inference -- optimal -- spatial correlation -- urban water system; Hydrology -- Data processing -- Periodicals; Geographic information systems -- Periodicals; Geographic information systems; Hydrology -- Data processing; Electronic journals; Periodicals; 551.480285
• DOI: 10.2166/hydro.2022.040
• Languages: English
• ISSNs: 1464-7141

Abstract

Abstract: 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.