Implications of the Value of Hydrologic Information to Reservoir Operations -- Learning From the Past
Hejazi, Mohamad Issa
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/83403
Description
Title
Implications of the Value of Hydrologic Information to Reservoir Operations -- Learning From the Past
Author(s)
Hejazi, Mohamad Issa
Issue Date
2009
Doctoral Committee Chair(s)
Cai, Ximing
Department of Study
Civil Engineering
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Hydrology
Language
eng
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).
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
