An Active-Learning Framework for Efficient Training and Bias Mitigation in Probabilistic Forecasting of Water Pipeline Failures
Bibliographic record
Abstract
Despite recent advancements in forecasting models for water pipe failures, their implementation remains challenging in many urban environments due to data scarcity. Because water pipe breaks are irregular and intermittent events, existing forecasting models rely on large data sets to achieve robust predictive accuracy. However, such data sets are often unavailable due to practical challenges and the high cost of water pipe condition assessments. Furthermore, small and incomplete data sets are prone to bias and imbalance, which further complicates the training process for forecasting models. To address these challenges, this study develops and empirically evaluates a novel active-learning mechanism that enhances forecasting models for water pipeline failure in two key ways: (1) enabling efficient model training with a significantly smaller data set by selecting the most informative observations, and (2) facilitating effective model training despite unbalanced and potentially biased data. The proposed active-learning mechanism is a progressive and iterative process built on four essential components: (1) stratified sampling through a multidimensional clustering mechanism, (2) cluster weight assignment, (3) a probabilistic forecasting model, and (4) a prediction deviation scoring method for each pipe in the test data. The proposed solution was implemented using historical data from Calgary, Canada. The results showed that the proposed active-learning framework, which selects observations for training, enabled an autoregressive deep-learning forecasting model to achieve a precision-recall area under the curve (PR-AUC) of 90% using only 42.5% of the data (6,052 pipes). In contrast, a similar forecasting model trained on randomly selected data required more than 80% of the data set (11,253 pipes) to reach the same predictive performance. These findings validate the effectiveness of the active-learning method in efficiently training forecasting models with small, unbalanced, and potentially biased data sets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".