Simulation and prediction of water temperature in a water transfer channel during winter periods using a new approach based on the wavelet noise reduction-deep learning method
Bibliographic record
Abstract
Abstract In winter, the water transfer channel of the Middle Route of South-to-North Water Transfer Project (MR-StNWTP) in China always encounters ice problems. The preciously simulation and prediction of water temperature is essential for analyzing the ice condition, which is important for the safety control of the water transfer channel in winter. Due to the difference of specific heat between water and air, when the air temperature rises and falls dramatically, the range of change of water temperature is relatively small and has a lag, which often affects the accuracy of simulation and prediction of water temperature based on air temperature. In the present study, a new approach for simulating and predicting water temperature in water transfer channels in winter has been proposed. By coupling the neural network theory to equations describing water temperature, a model has been developed for predicting water temperature. The temperature data of prototype observations in winter are preprocessed through the wavelet decomposition and noise reduction. Then, the wavelet soft threshold denoising method is used to eliminate the fluctuation of certain temperature data of prototype observations, and the corresponding water temperature is calculated afterward. Compared to calculation results using both general neural network and multiple regression approaches, the calculation results using the proposed model agree well with those of prototype measurements and can effectively improve the accuracy of prediction of water temperature.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".