Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models
Bibliographic record
Abstract
The model predictive control (MPC) approach can be implemented in either a reactive (RE-) or predictive (PR-) manner to control the operation of urban drainage systems (UDSs). Previous research focused mostly on the RE-MPC, as the PR-MPC, despite its potential to improve the performance of the UDS operations, requires additional computational resources and is more complex. This research evaluates the conditions under which the PR-MPC approach may be preferable. A PR-MPC model is developed, consisting of an adaptive input data-clustered ANN-based rainfall forecasting method coupled to an MPC framework. Observed and forecasted rainfall events are inputs to the internal MPC model, including the rainfall-runoff SWMM simulation model of the system and the MPC optimizer, which is a harmony search-based model determining optimal control policies. The proposed model was used as part of the UDS of Tehran, Iran, under different scenarios of input (rainfall), forecast accuracy (IFAC), and time horizon (IFTH). Results indicate that the PR-MPC performs better for longer-duration rainfall events, while the RE-MPC could be used to control very short storm occurrences. The proposed PR-MPC model can achieve between 85 and 92% of the performance of an ideal model functioning under the premise of perfect, error-free rainfall forecasts for two investigated rainfall events. Additionally, the IFAC can be improved by including rainfall fluctuations over finer temporal resolutions than the forecast horizon as additional predictors.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".