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  4. An Integrated Optimization Algorithm for Distributed Parameter Structure Identification in Groundwater Model Simulation
 
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An Integrated Optimization Algorithm for Distributed Parameter Structure Identification in Groundwater Model Simulation

Date Issued
2008
Date
2008
Author(s)
Tan, Chung-Che
URI
http://ntur.lib.ntu.edu.tw//handle/246246/181101
Abstract
The main contribution of this dissertation is proposing an integrated optimization algorithm for parameter structure identification in groundwater model simulation. The integrated optimization algorithm is applied to parameterization and parameter adjustment which are two essential components of solving inverse problems. As for parameterization, three zonation methods, Voronoi diagram (VD), multiplicatively weighted Voronoi diagram (MWVD), and pattern zonation (PZ), combined with the interpolation approach are presented in this dissertation to depict the spatial distribution of hydraulic conductivity. Each of these methods is able to be selected as a part of the integrated optimization algorithm. The simulated annealing algorithm (SA) and Tabu search (TS) are adopted in this dissertation to adjust the parameters. Moreover, the adjoint state method (ASM) and the coarse-fine grid search technique are allied with TS to enhance the efficiency in parameter adjustment.hree applications are represented in this dissertation to demonstrate the integrated optimization algorithm. First, SA and VD are used to identify the parameter structure of a confined aquifer. TS and three zonation methods are then applied to the parameter identification of hypothetical cases. TS allied with ASM are adopted with the coarse-fine grid search technique to identify both synthetically discrete and continuous hydraulic conductivity distribution efficiently and effectively. Meanwhile, the residual error, parameter uncertainty, structure error, and a modified Akaike Information Criterion values are used to help the determination of the model complexity over these applications. The results indicate that all SA, TS alone, and TS allied with ASM are able to optimize the parameter structure well.
Subjects
Parameter Identification
Inverse Problem
Simulated Annealing Algorithm
Tabu Search
Adjoint State Method
Parameter Uncertainty
Structure Error
Akaike Information Criterion
Type
thesis
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