HR: 0830h
AN: H21E-0896    [PDF]
TI: Stochastic Optimization For Water Resources Allocation
AU: * Yamout, G
EM: ghinay@ufl.edu
AF: University of Florida, 365 Weil Hall POBox 116580, Gainesville, Fl 32611-6580 United States
AU: Hatfield, K
EM: khatf@ufl.edu
AF: University of Florida, 365 Weil Hall POBox 116580, Gainesville, Fl 32611-6580 United States
AB: For more than 40 years, water resources allocation problems have been addressed using deterministic mathematical optimization. When data uncertainties exist, these methods could lead to solutions that are sub-optimal or even infeasible. While optimization models have been proposed for water resources decision-making under uncertainty, no attempts have been made to address the uncertainties in water allocation problems in an integrated approach. This paper presents an Integrated Dynamic, Multi-stage, Feedback-controlled, Linear, Stochastic, and Distributed parameter optimization approach to solve a problem of water resources allocation. It attempts to capture (1) the conflict caused by competing objectives, (2) environmental degradation produced by resource consumption, and finally (3) the uncertainty and risk generated by the inherently random nature of state and decision parameters involved in such a problem. A theoretical system is defined throughout its different elements. These elements consisting mainly of water resource components and end-users are described in terms of quantity, quality, and present and future associated risks and uncertainties. Models are identified, modified, and interfaced together to constitute an integrated water allocation optimization framework. This effort is a novel approach to confront the water allocation optimization problem while accounting for uncertainties associated with all its elements; thus resulting in a solution that correctly reflects the physical problem in hand.
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2003 Fall Meeting