Optimizing monitoring strategies for urban drainage systems via bilayer iterative clustering
Bibliographic record
Abstract
Online monitoring is increasingly essential for the effective management and operation of urban sewer systems, yet resource limitations necessitate careful planning of sensor deployment. This study aims to address the impact of time lags on monitoring point selection in urban drainage systems using unsupervised machine learning techniques. A novel method is introduced to determine the optimal number and placement of sensors in manholes, using cluster analysis informed by simulated time-series data. The proposed methodology involves two sequential stages: the first stage clusters time-series data based on morphology similarity using the time-lagged cross-correlation (TLCC) coefficient, which measures the temporal alignment between datasets. The second stage further refines these clusters by considering magnitude similarity, employing dynamic time warping distance to quantify shape-based similarities and improve clustering accuracy. The proposed approach allows for flexible threshold adjustments to accommodate specific engineering requirements, enabling the design of monitoring strategies tailored to a predetermined number of locations. Furthermore, the study explores the impact of rainfall intensity on sensor placement, providing actionable guidance for sewer managers to improve monitoring efficiency and address urban water management challenges.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".