Optimization Approaches for Reservoir Systems Operation Using Computational Intellegence Tools
Bibliographic record
Abstract
Soft computing based tools including Fuzzy Inference Systems (FIS), Artificial Neural Networks (ANN), and Genetic Algorithms (GA) are used here to tackle the optimization problem of large-scale reservoir operations. At first, a nonlinear programming optimization method develops the optimal release policy of the system. The policy is then simulated to provide the trajectory of optimal releases and storages of the reservoir for simulated stochastic inflows. These trajectories are then used as input-output data to train an Adaptive Neuro Fuzzy Inference System (ANFIS) to obtain updated fuzzy operating rules. A subtractive clustering algorithm is used to estimate the number of clusters and cluster centers in optimal data obtained from the optimization step to build an initial FIS. This initial FIS is then optimized using the ANFIS model. The ANFIS based fuzzy rules are simulated and compared with policies developed using a multiple regression analysis. In another test, a parameterized T-norm operator is applied and its parameters are optimized through GA. This post-optimization problem acts as a tool for variance reduction, which is otherwise a very hard optimization problem. The objective function in GA optimization minimizes the variance of the monthly supplies. Results compare the superior performance of the ANFIS-based policies over the multiple regression-based policies and also the usefulness of GA as a tool for variance reduction through optimizing the parameters of a T-norm fuzzy operator.
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.
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) | 0.002 | 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".