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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/12046

    Title: Application of Time Series Analysis on Temporal Variation of Fluoride in Groundwater around Southern Taiwan Science Park
    Authors: Ting-Nien Wu(吳庭年)
    Jan-Yee Lee
    Chen-Hsiang Huang
    Keywords: time series analysis
    groundwater contamination
    monitoring well
    data mining
    Date: 2010-08
    Issue Date: 2010-11-24 12:14:07 (UTC+8)
    Abstract: This paper demonstrated a case study on how to utilize
    time series analysis as mining tool to track the transience
    of fluoride release and to predict the fluoride
    concentration in groundwater. Southern Taiwan Science
    Park was selected as study area, and seven groundwater
    monitoring wells located in the domain of fluoride
    release were subjected to time series analysis. The
    measured fluoride levels in groundwater are between 0.4
    and 3.6 mg/L, and the series data is stationary in mean
    and variance during the period of 2005 and 2009. Time
    series analysis is a useful tool for extracting interesting
    pattern from ordered sequence of observations. Based on
    extracting information from ACF and PACF, the trend,
    interesting patterns, rules, or models within the original
    data set were filtered out. The common time series
    models, ARMA and ARIMA, were employed to interpret
    the information beneath the monitoring data of
    groundwater quality. Through verification by Akaike’s
    information criterion (AIC) and Schwartz’s Bayesian
    Information Criterion (SBC), the ARMA(1,1) model was
    identified as the best fitted model for data interpretation
    and estimation. Accordingly, this developed numerical
    model can effectively interpret and forecast the fluoride
    level in groundwater as referring the prior information.
    Appears in Collections:[環境工程系所] 期刊論文

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