Modelling And Research on Water Level Control of Great Lakes Based on Neural Network PID Algorithm
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
The Great Lakes are situated in the border region between the United States and Canada, which has a significant impact on the climate and the lives of those living in the surrounding areas. The objective of this paper is to establish a network of the Great Lakes through Pearson's correlation coefficient analysis and to construct a two-tank water level model based on a PID control system in order to effectively manage the dynamics of the Great Lakes. Firstly, the strength and direction of the linear relationship between two variables is quantified through Pearson's correlation coefficient analysis, which involves the collection of observational data and the calculation of mean values. This analysis serves as a fundamental basis for predictive modelling and hypothesis testing. Secondly, based on the flow balance principle, mathematical expressions are constructed to simulate the water flow, and a PID control system is constructed to achieve optimal water level maintenance. By analysing the Pearson's correlation coefficient, the interrelationships among the variables in the Great Lakes network can be understood, thereby providing guidance for scientific research and decision-making. The results demonstrate that the constructed two-tank water level model combined with the PID control system and SHAP algotithm can effectively manage the water level of the Great Lakes and achieve optimal water level regulation.
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 | 0.001 | 0.001 |
| 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 it