含水層非均質性對Papadopulous異向性分析影響之研究

Abstract

近來，經由現場抽水試驗，利用Papadopulous水層異向性分析方法 (PA), 可求得含水層異向性導水係數值及蓄水係數值。實際上，除非現場做過充 分的地質調查，由獲得的資料或證據顯示水層具有異向性，否則逕行假設 水層為均質並做水層異向性參數分析，極有可能由現場具非均質等向性的 水層，推算得異向性的水層參數值。本文擬針對上述問題，首先，基於 Papadopulous提出之異向性分析理論和非線性迴歸暨有限差分牛頓法，完 成簡稱為PANM的數值方法，以分析含水層異向性參數值。此外，利用亂數 場產生器，分別產生對數常態分布或具有空間相關結構的亂數值，代表非 均質水層之導水係數值，輸入二維地下水流數值模式，模擬非均質等向性 水層之現場抽水試驗，再利用PANM分析三組關測井的洩降時間數據，求解 代表場址的異向性參數值。最後，應用蒙地卡羅模擬方法，重複產生多組 導水係數值，以探討含水層非均質性對異向性分析之影響。利用 Papadopulous論文中所提供之三口觀測井洩降時間數據，以數值方法PANM 執行水層異向性分析，結果顯示PANM法較PA法為更簡單、更快速、更精確 之含水層異向性分析方法。此外，由模擬分析的結果，得到非均質等向性 水層之異向性參數值，證明含水層非均質性確實對異向性分析結果造成誤 差影響，且水層非均質性愈顯著，對異向性參數分析值精度的影響越大。 There are two approaches to predict the concentrations of atm- ospheric air pollutants. One is the dispersion modeling based on the physical or chemical phenomena of air pollutants, the other is statistical analysis which simply incorporates a group of monitoring data to find out forecasting rules. The purpose of this study is to analyze and forecast ambient hourly ozone concentrations by statistical time series analysis. Three time series models , ARIMA, ARIMA-Regression and ARIMA-Transfer Function,were evaluated and compared in this study. The results indicated that ARIMA-Transfer Function model provides the est forecasting ability. However, sometimes it is not possible to find resonable forecasting rules by ARIMA - Transfer Function due to the model's limitation. The ARIMA- Regression model, on the other hand, provides a good prediction ability in all cases. Results from the simplest model, ARIMA, is not so good as the other two models. However, the correlation coefficients between measured and predicted data made by ARIMA were above 0.8 in most of the cases. Therefore , time series analysis should be a good approach for analyzing and fore- casting the ozone levels in the atmosphere.