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oai:www.ideals.illinois.edu:2142/83403 2023-07-11 col_2142_5131 col_2142_14770 com_2142_5130 com_2142_3518 com_2142_234

Cai, Ximing Hejazi, Mohamad Issa 2015-09-25T21:04:41Z 2015-09-25T21:04:41Z 10000-01-01 2009 2009 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). Made available in DSpace on 2015-09-25T21:04:41Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3392065.pdf: 2319125 bytes, checksum: 3f631f94c062bf8ce4177d1e6e42a844 (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 84684 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 195 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/83403 (MiAaPQ)AAI3392065 eng Hydrology Implications of the Value of Hydrologic Information to Reservoir Operations -- Learning From the Past text Civil Engineering Civil Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.