Seamless integration of sewer system topology and tree location data: An algorithm to diagnose the potential impact of tree roots on pipes and propose rearrangement solutions
Bibliographic record
Abstract
Wastewater networks are subject to several threats leading to wastewater leakages and public health hazards. External elements such as natural factors and human activities are common causes of wastewater leakages and require more in-depth analysis. Prevention and rehabilitation work is essential to secure wastewater networks and avoid pipe failures. This work presents a new algorithm that allows for the seamless integration of sewer topology and tree location data to diagnose the potential impact of tree roots on pipes. The algorithm also proposes tree rearrangement options that balance the cost of tree rearrangement with the cost of pipe repair. The paper also showcases a real-world case study in the city of Girona to evaluate the performance of the presented algorithms for a specific case focusing on tree roots as a natural factor. Results show that it is possible to optimally rearrange a number of the trees with the greatest impact, significantly minimizing pipe failures and wastewater leakages (82% risk reduction with only rearranging a 12% of the most impactful trees). The rearrangement solution not only protects the environment and prevents public health hazards, but also achieves a positive economic payback during the operational period of the pipes, saving up to 1.33M€ for a tree rearrangement of 7%. The presented methodology is applicable to other natural or human factors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".