Runoff Predictions in a Semiarid Watershed by Convolutional Neural Networks Improved with Metaheuristic Algorithms and Forced with Reanalysis and Climate Data
Bibliographic record
Abstract
In this research, the role of climate variability and weather change in short-term streamflows, including extreme event, was investigated in semiarid climates. The deep learning convolutional neural networks (CNN) were modified by incorporating the imperialist competitive algorithm (ICA) and the grey wolf optimizer (GWO) method to improve hourly runoff predictions at multiple scales, ranging from 100 to over 6,000 km2 in the Seybouse Basin, Algeria. The atmospheric reanalysis data set, ECMWF Reanalysis v5 (ERA5) with a 31-km resolution, climate variability indices, and in situ runoff observations were used. The most relevant atmospheric and soil moisture predictors from the reanalysis grids covering the study area were used to represent spatial variability. The prediction performance of the original CNN and modified CNN-ICA and CNN-GWO models were evaluated. The CNN-GWO model outperformed CNN-ICA and the original model in predicting runoff and improved the Nash-Sutcliffe Efficiency score up to 0.99. Results across multiple scales disclose that the models with climate indices as inputs showed higher performance than the models with only atmospheric data as inputs, especially in predicting extreme runoff values in basins with elevations above 670 m, suggesting that climate variability indices need to be considered in flood predictions and infrastructure design in mountainous areas with increasing climate change uncertainties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".