Robust Multi-Lake Water-Level Regulation via Network-Flow–Informed PID Control with PSO Tuning and Global Sensitivity Analysis
Bibliographic record
Abstract
Effective water-level regulation across interconnected lakes is essential for flood prevention, ecological balance, and sustainable hydropower operation. This study proposes a hybrid control and optimization framework integrating physical network-flow modelling, constrained optimization, and intelligent control parameter tuning. First, the Great Lakes system is represented as a directed network that captures inflows, outflows, and hydrological couplings. The optimal target levels of each lake are determined using Sequential Least-Squares Quadratic Programming (SLSQP) under multi-objective constraints of ecological stability and energy efficiency. A proportional–integral–derivative (PID) controller is then established to regulate outflows, and its parameters are automatically tuned by Particle Swarm Optimization (PSO) to minimize a composite performance index consisting of steady-state error, overshoot, and rise time. Furthermore, a global sensitivity analysis based on the Sobol method is conducted to quantify the influence of hydrological and climatic factors—including precipitation, evaporation, snowmelt, and temperature—on water-level dynamics. Simulation results show that the optimized controller effectively tracks target water levels with reduced overshoot and shorter adjustment time compared with conventional PID control. The sensitivity results reveal that precipitation and snowmelt dominate overall variance, highlighting seasonal vulnerability. The proposed framework demonstrates strong robustness and adaptability, providing a reliable approach for large-scale lake system regulation and sustainable water resource management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".