dc.creator | Shahverdi, Kazem | es |
dc.creator | Maestre Torreblanca, José María | es |
dc.creator | Alamiyan-Harandi, Farinaz | es |
dc.creator | Tian, Xin | es |
dc.date.accessioned | 2020-10-06T17:42:45Z | |
dc.date.available | 2020-10-06T17:42:45Z | |
dc.date.issued | 2020-08 | |
dc.identifier.citation | Shahverdi, K., Maestre Torreblanca, J.M., Alamiyan-Harandi, F. y Tian, X. (2020). Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals. Water, 12 (9). Article number 2407. | |
dc.identifier.issn | EISSN 2073-4441 | es |
dc.identifier.uri | https://hdl.handle.net/11441/101748 | |
dc.description.abstract | Recently, a continuous reinforcement learning model called fuzzy SARSA (state, action,
reward, state, action) learning (FSL) was proposed for irrigation canals. The main problem related
to FSL is its convergence and generalization in environments with many variables such as large
irrigation canals and situations beyond training. Furthermore, due to its architecture, FSL may
require high computation demands during its learning process. To deal with these issues, this work
proposes a computationally lighter generalizing learned Q-function (GLQ) model, which benefits
from the FSL-learned Q-function, to provide operators with a faster and simpler mechanism to obtain
operational instructions. The proposed approach is tested for di erent water requests in the East
Aghili Canal, located in the southwest of Iran. Several performance indicators are used for evaluating
the GLQ model results, showing convergence in all the investigated cases and the ability to estimate
operational instructions (actions) in situations beyond training, delivering water with high accuracy
regarding several performance indicators. Hence, the use of the GLQ model is recommended for
determining the operational patterns in irrigation canals. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad DPI2017-86918-R | es |
dc.format | application/pdf | es |
dc.format.extent | 18 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Water, 12 (9). Article number 2407. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Agricultural water management | es |
dc.subject | FSL | es |
dc.subject | GLQ | es |
dc.title | Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | DPI2017-86918-R | es |
dc.relation.publisherversion | https://doi.org/10.3390/w12092407 | es |
dc.identifier.doi | 10.3390/w12092407 | es |
dc.journaltitle | Water | es |
dc.publication.volumen | 12 | es |
dc.publication.issue | 9 | es |
dc.publication.initialPage | Article number 2407 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |