Streamflow forecasting with uncertainty estimate using Bayesian learning for ANN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate site-specific streamflow forecasts along with uncertainty estimate are of particular importance for water resources planning and management. In the last decade, different types of artificial neural network (ANN) models have been shown as promising alternative methods for rainfall-runoff modeling. However, one of the critical issues with ANN based modeling remains the lack of confidence limits for the prediction results. Therefore, whatever the accuracy of the prediction values, there is a lack of reliability for practical applications. The Bayesian learning algorithm overcomes that limitation by providing uncertainty estimates of the predicted results. The present paper introduces a Bayesian learning approach for ANN modeling of daily streamflows implemented with a multilayer perceptron (MLP). The proposed model results are compared with those obtained from a multilayer perceptron trained with a 'scaled conjugate gradient' method. Overall, the model validation statistics and hydrograph comparison indicate that the Bayesian learning approach outperforms the conventional approach in almost all respects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it