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Record W4392505184 · doi:10.1080/02626667.2024.2324132

A systematic review of Muskingum flood routing techniques

2024· review· en· W4392505184 on OpenAlex
Aryan Salvati, Alireza Moghaddam Nia, Ali Salajegheh, Ataollah Shirzadi, Himan Shahabi, Ebrahim Ahmadisharaf, Dawei Han, John J. Clague

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHydrological Sciences Journal · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFlood mythRouting (electronic design automation)Computer scienceEnvironmental scienceGeographyComputer network

Abstract

fetched live from OpenAlex

Flood routing is an important topic in engineering hydrology and an integral part of flood management and hydrodynamic modelling studies. Muskingum and Muskingum-Cunge methods are commonly used in flood routing studies. Here, we perform a systematic review of research on linear and non-linear Muskingum and Muskingum-Cunge flood routing methods to document how they have contributed to the development of flood science. Specifically, we document the evolution of publications involving Muskingum flood routing methods between 1938 and 2021. Research gaps progressively filled over time include the use of optimization algorithms, and tools for uncertainty and sensitivity analysis. In addition, we document various case studies that were conducted to improve flood routing performance. The chronological analysis reveals performance improvement of Muskingum and Muskingum-Cunge models in terms of cost effectiveness, user friendliness and ease of use.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.169
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.040
GPT teacher head0.329
Teacher spread0.289 · 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