Combination of the Worst-Case Parameter, Covariance Analysis, and Heuristic Algorithms to Identify Parameter Structure
Date Issued
2009
Date
2009
Author(s)
Chou, Chueh-Hao
Abstract
The origin of uncertainty comes from that people don’t have sufficient information and thus feel ambiguous about unknown future results. Sources of uncertainty would be different according to the characteristic of problems at different fields, but the common point is that the existence of uncertainty indeed would have inevitable impacts to the decision-making results. The purpose of this study is to discuss how uncertainty occurs and how uncertainty influences when water quality models are applied to river quality management. Three types of uncertainty are discussed, including process uncertainty, input data uncertainty, and parameter uncertainty. In order to effectively combine the methodologies and tools used in this study, the modified Streeter-Phelps equation would be used to simulate water quality, and the simulation results would be compared with those from QUAL2E for discussing and quantifying differences when different water quality models are used. Unlike traditional parameter uncertainty analysis, this study uses an approach that can identify parameter with a simplified structure and assure its accuracy requirement for predetermined model application. The content of this methodology includes the classification of inverse problems, the definition of identifiability, the calculation of the worst-case parameter, the solution of a generalized inverse problem and so forth. From the above theories, prior information could be assessed whether it is sufficient or not, and covariance analysis is used to help understand the relationship among parameter structure complexity, modeling error, and parameter uncertainty. In this study, all optimization problems are solved by heuristic algorithms. A case would be designed to confirm the above methodologies.
Subjects
Uncertainty
the Worst-Case Parameter
Generalized Inverse Problem
Covariance Analysis
Heuristic Algorithm
Type
thesis
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