Forecasting High-Flow Discharges in a Flashy Catchment Using Multiple Precipitation Estimates as Predictors in Machine Learning Models
Bibliographic record
Abstract
The use of machine learning (ML) for predicting high river flow events is gaining prominence and among its non-trivial design decisions is the definition of the quantitative precipitation estimate (QPE) product included in the input dataset. This study proposes and evaluates the use of multiple concurrent QPEs to improve the performance of a ML model towards the forecasting of the discharge in a flashy urban catchment. Multiple extreme learning machine (ELM) models were trained with distinct combinations of QPEs from radar, reanalysis, and gauge datasets. Their performance was then assessed in terms of goodness of fit and contingency analysis for the prediction of high flows. It was found that multi-QPEs models overperformed the best of its single-QPE counterparts, with gains in Kling-Gupta efficiency (KGE) values up to 4.76% and increase of precision in detecting high flows up to 7.27% for the lead times in which forecasts were considered “useful”. The novelty of these results suggests that the implementation of ML models could achieve better performance if the predictive features related to rainfall data were more diverse in terms of data sources when compared with the currently predominant use of a single QPE product.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".