A genetic-algorithm based automatic model calibrator for the UBC watershed model
department or school: Applied Science, Faculty of
department or school: Civil Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Lan, Yao-Hung
issued: 2009-08-06T00:00:00
issued: 2001
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: In this study, an optimization and search technique, based on the genetic algorithms (GAs) approach, is successfully used to develop an automatic model calibrator for the UBC Watershed Model. Unlike the existing random search calibration procedure, which limits the number of simultaneously calibrated modeling parameters to groups of about three to six at a time, the new GA-based calibrator allows all modeling parameters to be simultaneously evaluated. Because of the non-linear interactions between the modeling parameters, the simultaneous evaluation of all modeling parameters is demonstrated to achieve a good model calibration efficiently and quickly. The fundamental components of GAs as inspired by the Darwinian principle of natural selection are explained in detail in order to develop a complete GA-based model calibrator. A flow chart is used to illustrate the computational implementation of the GA procedures. Why GAs can work efficiently in finding an optimal set of modeling parameter values is explained by the schema theory with mathematical proofs provided. To test the soundness of the GA code developed for the automatic calibrator of the UBC Watershed Model, two well-studied watersheds in the Province of British Columbia, Campbell River and Illecillewaet River, are used. The effects of genetic operators (crossover, niching and elitism) on GA search efficiency are individually demonstrated. To objectively determine the performance of a calibrated watershed model, the difference between the observed and simulated streamflows is statistically measured. Four statistical measures are evaluated: coefficient of linear correlation (or coefficient of determination), Nash & Sutcliffe coefficient of efficiency (e!), least squares objective function and least absolute difference objective function are introduced. GA computational experiments show that the Nash & Sutcliffe coefficient of efficiency (e!) exhibits the most consistently decreasing trend of streamflow volume e…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/11785
extent: 5917782 bytes
format: application/pdf
note: A GENETIC-ALGORITHM BASED AUTOMATIC MODEL CALIBRATOR FOR THE UBC WATERSHED MODEL by Y A O - H U N G L A N B.A.Sc. The University of British Columbia, 1998 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF CIVIL ENGINEERING We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA JULY, 2001 © Yao-HungLan,2001 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. The University of British Columbia Vancouver, Canada DE-6 (2/88) A B S T R A C T In this study, an optimization and search technique, based on the genetic algorithms (GAs) approach, is successfully used to develop an automatic model calibrator for the U B C Watershed Model. Unlike the existing random search calibration procedure, which limits the number of simultaneously calibrated modeling parameters to groups of about three to six at a time, the new GA-based calibrator allows all modeling parameters to be simultaneously evaluated. Because of the non-linear interactions between the modeling parameters, the simultaneous evaluation of all modeling parameters is demonstrated to achieve a good model calibration efficiently and quickly. The fundamental components of GAs as inspired by the Darwinian principle of natural selection are explained in detail in order to develop a complete GA-based model calibrator. A flow chart is used to illustrate the computational implementation of the G A procedures. Why GAs can work efficie…
has type: Thesis/Dissertation
date issued: 2001-11
is shown at: 10.14288/1.0063945
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/11785