Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada
Bibliographic record
Abstract
While numerous studies have investigated physically-based analytical approaches for estimating stream flow probability distributions and occurrences of overbank flow, these types of models are limited by their associated complexity to incorporate a wide range of data from all components of the hydrologic system to model their influence on river flows. Alternatively, the Generalized Structure Group Method of Data Handling (GS-GMDH) is a polynomial network approach used in this study to train and test models for daily and hourly times series flow prediction for riverine flooding using available data from 1990 to 2018 and 1996 to 2018, respectively. The model is found to accurately predict daily flows with R2, RMSE, MAE, Bias and NSE of 0.6441, 46.884, 6.700, 1.800 and 0.6441, respectively, for nine years of flow data in application to the Bow River in Alberta, Canada. Hourly flow data used to train (70%) and test (30%) the GS-GMDH model results in R2, RMSE, MAE, Bias and NSE of 0.998, 3.323, 0.997, 0.00438 and 0.998, respectively. The trained hourly model can predict up to 17 h in advance while maintaining R2 greater than 0.90. Horizontal error highlights a weakness in model performance, contrary to other evaluation statistics, due to presence of imitation error.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".