Rainfall Runoff Analysis using Artificial Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Background/Objective: The main objective of the present study is to conduct laboratory experiment for the generation of rainfall runoff data using rainfall simulator. For the validation this observed data, a model is establish for estimating observed runoff data using Artificial Neural Network (ANN) technique. Methods: A total 12 laboratory experiments were conducted using rainfall simulator to generate runoff hydrograph using various slope and rainfall intensity over the catchment. For the validation of observed runoff hydrograph data were simulate using ANN. The ANN model was developed using collected 1076 data point to compute runoff discharge. For developing ANN model, the available data were separated as 70% for training, 15% for testing and 15% for validation. Results: The predicted results using ANN model performed better estimation with observed values which is useful for water resources planning and management etc. For the testing of model performance Nash-Sutcliffe efficiency criteria were used which gives NSE greater than 95%. Conclusion: The comparison of observed and predicted runoff hydrograph reveals that the Artificial Neural Network (ANN) predicts the runoff data reasonably well in observed hydrograph. It is found that ANNs are promising tools not only in accurate modeling of complex processes but also in providing insight from the learned relationship, which would assist the modeler in understanding of the process under investigation as well as in evaluation of the model. Keywords: ANNs, Laboratory Experiments, Rainfall-Runoff, Rainfall Simulator
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it