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Record W4410340405 · doi:10.14796/jwmm.s549

Developing the IsoProbability Curves of Combination of Rainfall and Water Level for Urban Drainage Infrastructure Planning—A Case Study in Ho Chi Minh City

2025· article· en· W4410340405 on OpenAlexvenueno aff
Giang Song Lê, Hoa Thanh Thi Nguyen

Bibliographic record

VenueJournal of Water Management Modeling · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersViet Nam National University Ho Chi Minh CityHo Chi Minh City University of Technology and Education
KeywordsHo chi minhDrainageWater resource managementEnvironmental scienceEnvironmental planningHydrology (agriculture)GeographyGeologyGeotechnical engineeringCartographyScale (ratio)

Abstract

fetched live from OpenAlex

To ensure efficiency and cost-effectiveness, drainage systems often need the capability to handle drainage such that the overload phenomenon only occurs with a return period greater than a certain value specific to the system. For urban areas situated in low-lying regions and influenced by tides, the flow in the drainage system depends on two natural factors: rainstorm rainfall in the area (R), and water level at the outlet (H) in the time of rainstorm. Consequently, the return period of the overload phenomenon in the drainage system will be determined by the combination of R, H causing the overload. Before conducting hydraulic calculations, it is essential to construct probability curves for the R, H combinations. This article aims to introduce a method for constructing iso-value curves representing the return period of R, H combinations. The method is applied to the case of Ho Chi Minh City as an example.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.293
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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