Bayesian geoadditive water pipe failure forecasting model by optimizingthe updating period
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".