Data Mining Algorithms for Water Main Condition Prediction—Comparative Analysis
Bibliographic record
Abstract
Accurate prediction of water mains condition is critical for effective rehabilitation planning. Advances in machine learning techniques can improve condition predictions. This paper compares the capabilities of various data mining techniques in predicting the condition of water mains. Predictive models investigated include generalized linear model, deep learning, decision tree, random forest, XGBoost, AdaBoost, and support vector machines. Models are first constructed leveraging a portion of the City of Waterloo, Canada, database. Genetic algorithm and cross-validation are then employed to optimize the hyperparameter tuning process. Several performance metrics and statistical tests are employed to compare the performance of the developed models utilizing a new set of data not previously used. The XGBoost model yielded the most promising results, with a mean relative error of 1.29%. Water main conditions are numerically represented on a scale from 0 to 10, with 10 indicating the highest condition. Extensive sensitivity analysis is conducted to obtain deeper insights into the most critical attributes for condition prediction. The developed model may help city managers develop optimal rehabilitation and renewal plans, considering the current and expected condition of their pipe inventory.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".