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    The Predictions of Air Pollution Levels by Nonphysical Models Based on Kalman Filtering Method

    Source: Journal of Dynamic Systems, Measurement, and Control:;1976:;volume( 098 ):;issue: 004::page 375
    Author:
    Y. Sawaragi
    ,
    T. Soeda
    ,
    H. Ishihara
    ,
    T. Yoshimura
    ,
    S. Ohe
    ,
    Y. Chujo
    DOI: 10.1115/1.3427054
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: We describe the applications of multiple linear regression model and auto-regressive model which may be of use for the on-line prediction and control of concentration levels of pollutants of air pollution. At the beginning of these researches, in this paper, are presented the prediction of air pollution levels at a few hours in advance. The state variables of the multiple linear regression model are determined by considering the contribution of the component analysis. Practical data measured in Tokyo and Tokushima prefecture in Japan are used, respectively. Kalman filtering method is utilized for the prediction by the multiple linear regression model. Auto-regressive model is fitted to the time series which is processed by subtracting the moving average from the original observed data sequence. Accuracy and characteristic of the prediction by the models presented here are compared with the model of the Box and Jenkins, and with that obtained by the principle of persistence, respectively. Both are found to be significantly more accurate and useful than these models.
    keyword(s): Filtration , Air pollution , Regression models , Automobiles , Time series AND Pollution ,
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      The Predictions of Air Pollution Levels by Nonphysical Models Based on Kalman Filtering Method

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/88431
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    • Journal of Dynamic Systems, Measurement, and Control

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    contributor authorY. Sawaragi
    contributor authorT. Soeda
    contributor authorH. Ishihara
    contributor authorT. Yoshimura
    contributor authorS. Ohe
    contributor authorY. Chujo
    date accessioned2017-05-08T23:00:19Z
    date available2017-05-08T23:00:19Z
    date copyrightDecember, 1976
    date issued1976
    identifier issn0022-0434
    identifier otherJDSMAA-26041#375_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/88431
    description abstractWe describe the applications of multiple linear regression model and auto-regressive model which may be of use for the on-line prediction and control of concentration levels of pollutants of air pollution. At the beginning of these researches, in this paper, are presented the prediction of air pollution levels at a few hours in advance. The state variables of the multiple linear regression model are determined by considering the contribution of the component analysis. Practical data measured in Tokyo and Tokushima prefecture in Japan are used, respectively. Kalman filtering method is utilized for the prediction by the multiple linear regression model. Auto-regressive model is fitted to the time series which is processed by subtracting the moving average from the original observed data sequence. Accuracy and characteristic of the prediction by the models presented here are compared with the model of the Box and Jenkins, and with that obtained by the principle of persistence, respectively. Both are found to be significantly more accurate and useful than these models.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleThe Predictions of Air Pollution Levels by Nonphysical Models Based on Kalman Filtering Method
    typeJournal Paper
    journal volume98
    journal issue4
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.3427054
    journal fristpage375
    journal lastpage386
    identifier eissn1528-9028
    keywordsFiltration
    keywordsAir pollution
    keywordsRegression models
    keywordsAutomobiles
    keywordsTime series AND Pollution
    treeJournal of Dynamic Systems, Measurement, and Control:;1976:;volume( 098 ):;issue: 004
    contenttypeFulltext
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