Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models
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 o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/10/7/139 |
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| author | Fatemeh Jafari S. Jamshid Mousavi Kumaraswamy Ponnambalam |
| author_facet | Fatemeh Jafari S. Jamshid Mousavi Kumaraswamy Ponnambalam |
| author_sort | Fatemeh Jafari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:02:08Z |
| format | Article |
| id | doaj.art-e26f9b9216e3433ba4d5ea9a50c4833f |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-11T01:02:08Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-e26f9b9216e3433ba4d5ea9a50c4833f2023-11-18T19:35:52ZengMDPI AGHydrology2306-53382023-06-0110713910.3390/hydrology10070139Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting ModelsFatemeh Jafari0S. Jamshid Mousavi1Kumaraswamy Ponnambalam2Faculty of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 5R5, CanadaFaculty of Civil and Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15875-4413, IranDepartment of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 5R5, CanadaThe 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.https://www.mdpi.com/2306-5338/10/7/139urban drainage systemsshort-term rainfall forecastingartificial neural networksmodel predictive controlflood controlharmony search optimization |
| spellingShingle | Fatemeh Jafari S. Jamshid Mousavi Kumaraswamy Ponnambalam Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models Hydrology urban drainage systems short-term rainfall forecasting artificial neural networks model predictive control flood control harmony search optimization |
| title | Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models |
| title_full | Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models |
| title_fullStr | Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models |
| title_full_unstemmed | Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models |
| title_short | Predictive MPC-Based Operation of Urban Drainage Systems Using Input Data-Clustered Artificial Neural Networks Rainfall Forecasting Models |
| title_sort | predictive mpc based operation of urban drainage systems using input data clustered artificial neural networks rainfall forecasting models |
| topic | urban drainage systems short-term rainfall forecasting artificial neural networks model predictive control flood control harmony search optimization |
| url | https://www.mdpi.com/2306-5338/10/7/139 |
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