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Fuzzy logic modeling of surface ozon...

Mintz, Rachel.

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Fuzzy logic modeling of surface ozone concentrations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fuzzy logic modeling of surface ozone concentrations./
Author:	Mintz, Rachel.
Description:	148 p.
Notes:	Source: Masters Abstracts International, Volume: 43-02, page: 0574.
Contained By:	Masters Abstracts International43-02.
Subject:	Engineering, Chemical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ93624
ISBN:	0612936244

Fuzzy logic modeling of surface ozone concentrations.
Mintz, Rachel.

Fuzzy logic modeling of surface ozone concentrations. - 148 p.

Source: Masters Abstracts International, Volume: 43-02, page: 0574.

Thesis (M.Sc.)--University of Calgary (Canada), 2004.

Fuzzy logic is a methodology based on the principle that variables are often imprecise or uncertain. For this reason, fuzzy logic provides effective solutions for nonlinear and partially unknown systems. Due to the complex relationships and the necessity for forecasts in atmospheric studies, air pollution modeling is a task for which fuzzy logic methods are amicably suited. This research investigates the ability to predict surface ozone concentration with the use of an automated fuzzy logic method, termed Modified Learning from Examples (MLFE). The MLFE method analyzes training data one-by-one in order to generate a series of rules describing the system. This method is computationally simple and easy to execute, provided the selection of training data and tuning parameters are "good".

ISBN: 0612936244Subjects--Topical Terms:

1018531
Engineering, Chemical.

Fuzzy logic modeling of surface ozone concentrations.
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100 1 $a Mintz, Rachel. $3 1939670
245 1 0 $a Fuzzy logic modeling of surface ozone concentrations.
300 $a 148 p.
500 $a Source: Masters Abstracts International, Volume: 43-02, page: 0574.
500 $a Adviser: William Y. Surcek.
502 $a Thesis (M.Sc.)--University of Calgary (Canada), 2004.
520 $a Fuzzy logic is a methodology based on the principle that variables are often imprecise or uncertain. For this reason, fuzzy logic provides effective solutions for nonlinear and partially unknown systems. Due to the complex relationships and the necessity for forecasts in atmospheric studies, air pollution modeling is a task for which fuzzy logic methods are amicably suited. This research investigates the ability to predict surface ozone concentration with the use of an automated fuzzy logic method, termed Modified Learning from Examples (MLFE). The MLFE method analyzes training data one-by-one in order to generate a series of rules describing the system. This method is computationally simple and easy to execute, provided the selection of training data and tuning parameters are "good".
520 $a MLFE ozone models are developed using meteorological inputs for the cities of Edmonton and Calgary in which a framework for the selection of training data and tuning parameters is explored. Hourly ozone concentrations during summer months in Edmonton are predicted with the MLFE model and the results are compared to models used by Environment Canada. The Root Mean Square Error, Mean Absolute Error, and scatter plots are used to compare the results of the MLFE, CHRONOS and CANFIS models. Ozone concentrations during the winter months in Calgary are modeled, in which special attention is given to the meteorological occurrence of Chinook winds. The newly developed models capture the trends in ozone concentration, and based on the statistical comparisons, the MLFE consistently shows good agreement with the measured meteorological data.*
520 $a *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Microsoft Office.
590 $a School code: 0026.
650 4 $a Engineering, Chemical. $3 1018531
650 4 $a Physics, Atmospheric Science. $3 1019431
650 4 $a Environmental Sciences. $3 676987
650 4 $a Artificial Intelligence. $3 769149
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710 2 0 $a University of Calgary (Canada). $3 1017619
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790 1 0 $a Surcek, William Y., $e advisor
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791 $a M.Sc.
792 $a 2004
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