Research on Lake Water Level Control Based on Simulated Annealing Multi-Objective Planning Modeling
Bibliographic record
Abstract
This paper focuses on the water level management problem of the Great Lakes, and solves the problem of solving the water level control problem by constructing a network flow model and an intelligent perception model. First, we constructed network maps of the Great Lakes and rivers using the collected water level data, and derived the optimal water level of the Great Lakes at any time of the year through a multi-objective planning model based on simulated annealing. Based on this, we constructed network flow maps and optimization-oriented models for the lakes and rivers. Next, we gradually added dam influences and continued with simulated water level control. Evaluation based on the entropy weight-TOPSIS model yielded an optimization model score of 0.743, while the actual water level score was 0.428, which indicated that our model better controlled the water level. Finally, based on the AHP model in order to determine the weights of each stakeholder, it was found that shipping companies have the greatest influence on the water level of Lake Ontario. Also, through multiple linear regression modeling, we found that the four major lakes and the St. Lawrence River have a greater influence on Lake Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".