Semidistributed Form of the Tank Model Coupled with Artificial Neural Networks
Bibliographic record
Abstract
Traditional conceptual rainfall–runoff models in the lumped form are usually developed without consideration of the spatial variation of rainfall and the heterogeneity of the watershed geomorphological nature. As an improvement to traditional conceptual models of the lumped form, a semidistributed form of the Tank model coupled with artificial neural networks (ANNs) is proposed herein. As a result, the effect of spatial variations of rainfall and model parameters can be investigated by dividing the entire catchment into a number of subcatchments and applying the spatially varied rainfall inputs and parameters to each subcatchment. Furthermore, in contrast to the linear summation commonly used in watershed routing that usually regards the total simulated runoff at the entire catchment outlet as a linear superposition of the routed runoff from all individual subcatchments, artificial neural networks are employed to explore nonlinear transformations of the runoff generated from the individual subcatchments into the total runoff at the entire watershed outlet. As illustrated in this study, coupling ANNs with traditional conceptual models reveals a promising new approach to catchment rainfall-runoff modeling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".