A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers
Links
- More information: Publisher Index Page (via DOI)
- Download citation as: RIS | Dublin Core
Abstract
ABSTRACT: In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers |
| Series title | Journal of the American Water Resources Association |
| DOI | 10.1111/j.1752-1688.1998.tb01525.x |
| Volume | 34 |
| Issue | 4 |
| Publication Date | June 08, 2007 |
| Year Published | 1998 |
| Language | English |
| Publisher | American Water Resources Association |
| Description | 12 p. |
| First page | 909 |
| Last page | 920 |