dc.creator | Milašinović, Miloš | |
dc.creator | Prodanović, Dušan | |
dc.creator | Stanić, Miloš | |
dc.creator | Zindović, Budo | |
dc.creator | Stojanović, Boban | |
dc.creator | Milivojević, Nikola | |
dc.date.accessioned | 2022-08-03T07:55:51Z | |
dc.date.available | 2022-08-03T07:55:51Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 1464-7141 | |
dc.identifier.uri | https://grafar.grf.bg.ac.rs/handle/123456789/2693 | |
dc.description.abstract | Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy to apply, they still fail to provide reliable forecasts during extreme periods due to a lack of training data. Therefore, model-driven forecasting is still needed. However, the model-driven forecasting approach is affected by numerous uncertainties in initial and boundary conditions. To improve the real-time model's operation, it can be regularly updated using measured data in the data assimilation (DA) procedure. Widely used DA techniques are computationally expensive, which reduce their real-time applications. Previous research shows that tailor-made, time-efficient DA methods based on the control theory could be used instead. This paper presents further insights into the control theory-based DA for 1D hydraulic models. This method uses Proportional–Integrative–Derivative (PID) controllers to assimilate computed water levels and observed data. This paper describes the two-stage PID controllers’ tuning procedure. Multi-objective optimization by Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to determine optimal parameters for PID controllers. The proposed tuning procedure is tested on a hydraulic model used as a decision support tool for the transboundary Iron Gate 1 hydropower system on the Danube River, showing that the average discrepancy between modeled and observed water levels can be less than 0.05 m for more than 97% of assimilation window. | sr |
dc.language.iso | en | sr |
dc.publisher | IWA Publishing | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/inst-2020/200092/RS// | |
dc.rights | openAccess | sr |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | Journal of Hydroinformatics | sr |
dc.subject | data assimilation | sr |
dc.subject | NSGA-II | sr |
dc.subject | PID controllers | sr |
dc.subject | tuning controllers | sr |
dc.title | Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization | sr |
dc.type | article | sr |
dc.rights.license | BY-NC-ND | sr |
dc.citation.issue | 4 | |
dc.citation.rank | M22~ | |
dc.citation.volume | 24 | |
dc.identifier.doi | 10.2166/hydro.2022.034 | |
dc.identifier.fulltext | http://grafar.grf.bg.ac.rs/bitstream/id/10407/bitstream_10407.pdf | |
dc.type.version | publishedVersion | sr |