Bibliographic record
Abstract
Automated Detection of Urban Flooding from News Farzaneh Zarei and Mazdak Nik-Bakht Pages 515-520 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Although minor overflows do not cause a huge amount of loss; as the number of such overflows increases, the amount of water wasted, and the compound consequent challenges become considerable. Therefore, detecting overflows, investigating their cause root and resolving them in a timely manner are among new needs for infrastructure managers. This paper suggests a new method for detecting distributed water overflows by extracting the flood information (such as the date and location of the incident) from the news. As a case study, we crawled Montreal newspaper and news websites to detect the related news to urban flooding and their detailed information. We trained several classifiers to identify news relevant to flood. Our experiments illustrate that by applying mutual information method for feature selection and employing support vector machine (SVM) architecture as the classifier, relevant news can be detected with an accuracy (F-measure) of above 80%. Such actionable information can help infrastructure managers with a wide range of decisions from repair and maintenance of existing systems, to capacity evaluation for new designs. Keywords: Classification; Montreal newspaper; Urban Flooding; Water overflows DOI: https://doi.org/10.22260/ISARC2019/0069 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".