Deep learning reveals the relationship between vegetation and runoff in the Weihe River Basin on the Loess Plateau
Bibliographic record
Abstract
The Weihe River Basin, a typical watershed in the Loess Plateau, is used as the research object to examine the regulation of runoff by vegetation. Here, we employed two deep learning algorithms, FNN and LSTM, for runoff modelling and adopted two modelling schemes—one incorporating NDVI and the other excluding NDVI—to examine the role of vegetation in runoff simulation. Finally, the optimal algorithm and the most appropriate modelling scheme were applied to simulate runoff variations under different scenarios. The results showed that the overall trend of NDVI in the Weihe River basin is increasing, and the overall trend of runoff is decreasing. Incorporating NDVI into the models will significantly improve the simulation accuracy. We also found that intra-annual vegetation cover variations impact runoff processes, with wet and normal year runoff highly sensitive to these changes. Our study highlights the role of vegetation in regulating runoff.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".