Performance evaluation of ANN and geomorphology-based models for runoff and sediment yield prediction for a Canadian watershed
Bibliographic record
Abstract
Artificial Neural Network (ANN) and regression models were developed using watershed-scale geomorphologic parameters to predict surface runoff and sediment losses of the St. Esprit watershed, Quebec, Canada. Ge omorphological parameters describing the land surface drainage characteristics and surface water flow behaviour were empirically associated with measured rainfall and runoff data and used as input to a three-layered back-propagation feed-forward neural network model. Morphological parameters such as bifu rcation ratio, area ratio, channel length ratio, drainage factor and relief ratio were selected using t he Multivariate Adaptive Regression Splines tool, based on their relative impo rtance in prediction of runoff and sediment yield. R egression models were developed using the curve-fitting toolbox of MATLAB software and compared with the results obtained from ANN models. The coefficient of determination (R 2 ) and model efficiency factor (E) were estimated to ascertain the model performance. Geomorphology-based ANN model validation statistics resulted in R 2 values ranging from 0.85 to 0.95 and E values from 0.74 to 0.82 for peak runoff rate and R 2 values from 0.78 to 0.93 and E values from 0.71 to 0.76 for sediment loss. Using geomorphology-based regression models, R 2 values for the same dataset varied from 0.78 to 0.88 (0.74 > E > 0.69) for peak runoff rate prediction and 0.39 to 0.54 (0.53 > E > 0.46) for sediment prediction. When morphological parameters were not associated with rainfall depth and peak runoff rate, prediction error statistical parameter values ( R 2 and E) were less for both neural network and regression models. Thus, associating selected geomorphological parameters with rainfall depth and peak runoff rate enhances the accuracy of runoff rate and sediment loss predictions from the watershed. Furthermore, ANN models performed better than the regression equations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".