{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83403"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83403","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Implications of the Value of Hydrologic Information to Reservoir Operations -- Learning From the Past","abstract":"Finally we couple the data mining procedure with conventional reservoir optimization techniques to build an enhanced stochastic dynamic programming (SDP) model. The enhanced SDP model is applied to the Shelbyville Reservoir, IL, and then compared to two classic SDP formulations. From a data mining procedure, past month's inflow, current month's inflow, past month's release, and past month's Palmer drought severity index are found to be important state variables in the enhanced SDP model formulations for Shelbyville Reservoir. The study indicates that the enhanced SDP model resembles historical records more closely yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions).","abstract_html":"Finally we couple the data mining procedure with conventional reservoir optimization techniques to build an enhanced stochastic dynamic programming (SDP) model. The enhanced SDP model is applied to the Shelbyville Reservoir, IL, and then compared to two classic SDP formulations. From a data mining procedure, past month's inflow, current month's inflow, past month's release, and past month's Palmer drought severity index are found to be important state variables in the enhanced SDP model formulations for Shelbyville Reservoir. The study indicates that the enhanced SDP model resembles historical records more closely yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions).","abstract_has_math":false,"creators":["Hejazi, Mohamad Issa"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Cai, Ximing"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T21:04:41Z","date_published":"2015-09-25T21:04:41Z","updated_at":"2026-07-22T22:26:21Z","subjects":["Hydrology"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3392065"],"render_values":[{"text":"(MiAaPQ)AAI3392065","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83403","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":["Hejazi, Mohamad Issa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T21:04:41Z","10000-01-01","2009"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Hydrology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/83403","(MiAaPQ)AAI3392065"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Finally we couple the data mining procedure with conventional reservoir optimization techniques to build an enhanced stochastic dynamic programming (SDP) model. 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The enhanced SDP model is applied to the Shelbyville Reservoir, IL, and then compared to two classic SDP formulations. From a data mining procedure, past month's inflow, current month's inflow, past month's release, and past month's Palmer drought severity index are found to be important state variables in the enhanced SDP model formulations for Shelbyville Reservoir. The study indicates that the enhanced SDP model resembles historical records more closely yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions).","Made available in DSpace on 2015-09-25T21:04:41Z (GMT). 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