{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113159"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113159","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Developing a data-driven model for dynamic reservoir operation using a combined hidden Markov-decision tree and classification tree algorithms","abstract":"Reservoir operations are faced with greater challenges than before due to growing water demands and climate change, and thus understanding and improvement of reservoir operations are critical. This study extends the hidden-Markov-decision tree (HM-DT) model developed by Zhao and Cai (2020) and proposes a data-driven reservoir operation model (DROM). The HM-DT model is first applied to individual reservoirs to derive sets of representative operation modules. Then a module classification model based on the Classification and Regression-tree algorithm is developed to determine which module to use for a day. DROM combines the derived operation modules and the module classification model to realize daily release prediction. DROM is tested with 25 reservoirs operated by USBR in north Great Plains regions, and it is shown that DROM can achieve acceptable accuracy in simulating historical releases (NSE > 0.4) and predicting future releases (NSE > 0.2) for 23 reservoirs. Compared with existing data-driven models, DROM shows several advantages including easily satisfied data requirements, transparent model structure, and broad applicability to various reservoirs. Especially, DROM can simulate the dynamic operation patterns through choosing the modules, while other previous models can only derive static operation rules. DROM can be used to better understand real-world reservoir operation behaviors and to explore the improvement of operation via combining with optimization models.","abstract_html":"Reservoir operations are faced with greater challenges than before due to growing water demands and climate change, and thus understanding and improvement of reservoir operations are critical. This study extends the hidden-Markov-decision tree (HM-DT) model developed by Zhao and Cai (2020) and proposes a data-driven reservoir operation model (DROM). The HM-DT model is first applied to individual reservoirs to derive sets of representative operation modules. Then a module classification model based on the Classification and Regression-tree algorithm is developed to determine which module to use for a day. DROM combines the derived operation modules and the module classification model to realize daily release prediction. DROM is tested with 25 reservoirs operated by USBR in north Great Plains regions, and it is shown that DROM can achieve acceptable accuracy in simulating historical releases (NSE > 0.4) and predicting future releases (NSE > 0.2) for 23 reservoirs. Compared with existing data-driven models, DROM shows several advantages including easily satisfied data requirements, transparent model structure, and broad applicability to various reservoirs. Especially, DROM can simulate the dynamic operation patterns through choosing the modules, while other previous models can only derive static operation rules. DROM can be used to better understand real-world reservoir operation behaviors and to explore the improvement of operation via combining with optimization models.","abstract_has_math":false,"creators":["Chen, Yanan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Cai, Ximing"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:35:06Z","date_published":"2022-01-12T22:35:06Z","updated_at":"2026-07-22T22:24:53Z","subjects":["reservoir operation simulation","hidden Markov-decision tree model","classification tree"],"languages":["en"],"rights":["Copyright 2021 Yanan Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113159","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Cai, Ximing"]},{"key":"dc:creator","label":"Author","values":["Chen, Yanan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:35:06Z","2024-01-12T22:35:30Z","2021-07-13","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["reservoir operation simulation","hidden Markov-decision tree model","classification tree"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Yanan Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113159"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Reservoir operations are faced with greater challenges than before due to growing water demands and climate change, and thus understanding and improvement of reservoir operations are critical. This study extends the hidden-Markov-decision tree (HM-DT) model developed by Zhao and Cai (2020) and proposes a data-driven reservoir operation model (DROM). The HM-DT model is first applied to individual reservoirs to derive sets of representative operation modules. Then a module classification model based on the Classification and Regression-tree algorithm is developed to determine which module to use for a day. DROM combines the derived operation modules and the module classification model to realize daily release prediction. DROM is tested with 25 reservoirs operated by USBR in north Great Plains regions, and it is shown that DROM can achieve acceptable accuracy in simulating historical releases (NSE > 0.4) and predicting future releases (NSE > 0.2) for 23 reservoirs. Compared with existing data-driven models, DROM shows several advantages including easily satisfied data requirements, transparent model structure, and broad applicability to various reservoirs. Especially, DROM can simulate the dynamic operation patterns through choosing the modules, while other previous models can only derive static operation rules. DROM can be used to better understand real-world reservoir operation behaviors and to explore the improvement of operation via combining with optimization models.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Yanan Chen, accepted the attached license on 2021-07-08 at 14:07.","The student, Yanan Chen, submitted this Thesis for approval on 2021-07-08 at 14:18.","This Thesis was approved for publication on 2021-07-13 at 11:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16805 on 2022-01-12 at 12:54:01","Made available in DSpace on 2022-01-12T22:35:06Z (GMT). No. of bitstreams: 2 CHEN-THESIS-2021.pdf: 7813556 bytes, checksum: 6ca19c69308038836ab76c8666663858 (MD5) LICENSE.txt: 4207 bytes, checksum: f64ac30279cbb29806f4b2aa22f52d19 (MD5) Previous issue date: 2021-07-13","Embargo set by: Seth Robbins for item 121085 Lift date: 2024-01-12T22:35:30Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Developing a data-driven model for dynamic reservoir operation using a combined hidden Markov-decision tree and classification tree algorithms"]}]}],"canonical_facts":{"dc:contributor":["Cai, Ximing"],"dc:creator":["Chen, Yanan"],"dc:date":["2022-01-12T22:35:06Z","2024-01-12T22:35:30Z","2021-07-13","2021-08"],"dc:description":["Reservoir operations are faced with greater challenges than before due to growing water demands and climate change, and thus understanding and improvement of reservoir operations are critical. This study extends the hidden-Markov-decision tree (HM-DT) model developed by Zhao and Cai (2020) and proposes a data-driven reservoir operation model (DROM). The HM-DT model is first applied to individual reservoirs to derive sets of representative operation modules. Then a module classification model based on the Classification and Regression-tree algorithm is developed to determine which module to use for a day. DROM combines the derived operation modules and the module classification model to realize daily release prediction. DROM is tested with 25 reservoirs operated by USBR in north Great Plains regions, and it is shown that DROM can achieve acceptable accuracy in simulating historical releases (NSE > 0.4) and predicting future releases (NSE > 0.2) for 23 reservoirs. Compared with existing data-driven models, DROM shows several advantages including easily satisfied data requirements, transparent model structure, and broad applicability to various reservoirs. Especially, DROM can simulate the dynamic operation patterns through choosing the modules, while other previous models can only derive static operation rules. DROM can be used to better understand real-world reservoir operation behaviors and to explore the improvement of operation via combining with optimization models.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Yanan Chen, accepted the attached license on 2021-07-08 at 14:07.","The student, Yanan Chen, submitted this Thesis for approval on 2021-07-08 at 14:18.","This Thesis was approved for publication on 2021-07-13 at 11:29.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16805 on 2022-01-12 at 12:54:01","Made available in DSpace on 2022-01-12T22:35:06Z (GMT). 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