Journal
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE
Volume 126, Issue 2, Pages 48-56Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)0733-9496(2000)126:2(48)
Keywords
-
Categories
Ask authors/readers for more resources
Heuristic search techniques are highly flexible but computationally intensive optimization methods that require hundreds, sometimes thousands, of evaluations of the objective function to reach termination criteria in common water resources optimization applications. One way to make these techniques more tractable when the objective function depends on a time-consuming flow and transport model is to employ an empirical approximation of the model. The current study examines the impact of employing artificial neural networks (ANNs) and linear approximators (LAs) on the quality and quantity of solutions obtained from simulated annealing-driven searches on two different ground-water remediation problems. The quality of results obtained when ANNs served as substitutes for the full model was consistently comparable to that of results obtained when the full model itself was called in the course of the search. The effect on quality of results of substituting an LA for the full model was more variable.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available