Long lead forecasting of spring peak runoff using Mamdani-type fuzzy logic systems at Hay River, NWT
Bibliographic record
Abstract
In northern riverside communities, breakup ice jam flooding is an annual threat to properties and human lives. In this study, the peak snowmelt runoff during breakup was assessed as an indicator of breakup flood severity. Due to the sparse network of hydrometeorolocial data in remote northern regions, a Mamdani-type fuzzy logic system (FLS) was developed and tested with the limited historical data. Three input variables were defined from the precipitation, air temperature and daily water level data. All of these variables are known ∼3 to 4 weeks before breakup enabling a long lead-time forecast. The process of system development is demonstrated by a case study of the Town of Hay River, NWT Canada. A series of experiments were designed to select the best system configuration, which also provided a way to conduct a sensitivity analysis for different choices in each system component. It was found that the system shows very good performance on the historical data using the qualitative error index. The results of the sensitivity analysis suggest the system performance is dependent on the choices of fuzzy logic inference operators and defuzzification method. As a long lead system, the short-term meteorological factors that would affect the system output were analyzed and the possible error range was assessed. Preliminary model validation, based on three years of testing, shows promising performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".