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論文中文名稱:機率為基礎含水層脆弱度評估模式之發展-以濁水溪沖積扇為例 [以論文名稱查詢館藏系統]
論文英文名稱:Developing a Probability-based Model of Aquifer Vulnerability Assessment in Choushui River
alluvial fan [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:工程學院
系所名稱:土木與防災研究所
畢業學年度:100
出版年度:101
中文姓名:彭怡惠
英文姓名:Yi-Huei Peng
研究生學號:99428083
學位類別:碩士
語文別:中文
口試日期:2012-07-08
論文頁數:97
指導教授中文名:陳世楷;張誠信
口試委員中文名:王聖瑋;高雨瑄
中文關鍵詞:DRASTIC污染潛勢地下水地理統計機率相關性分析
英文關鍵詞:DRASTICContamination PotentialGroundwaterGeostatisticsProbabilitycorrelation analysis
論文中文摘要:地下水是極為重要的水資源之一,相較於地表水而言,地下水具有成本低廉,水溫與水量穩定、水質良好及取用方便等多項優點。目前台灣西南部地區仍大量抽取地下水供應灌溉、養殖、民生及工業用水,若地下水遭受污染,勢必嚴重影響該地區之水資源供需平衡,甚至對人體健康造成危害,故地下水資源保育業已成為極重要之議題。DRASTIC含水層脆弱度模式自發展以來經常被使用於評估地下水污染潛勢,但此模式在應用上常因相關參數資料稀少,易產生高度不確定性,進而影響模式之評估結果。因此本研究應用指標為基礎之地理統計發展以機率為基礎之DRASTIC含水層脆弱度評估模式,除估算地下水污染潛勢值外,並評估其污染潛勢預測表現。本研究將此法實際應用於濁水溪沖積扇,DRASTIC模式中之各參數分別使用三種選取方法(最大機率選取、期望值選取、傳統選取法)進行污染潛勢評估,研究結果顯示,高污染潛勢區皆位於沖積扇之扇頂區,土地利用型態以農業耕種為主,而中度污染潛勢則大多分佈於扇央地區,無污染及低度風險污染大多分佈於沖積扇西部沿海地區以及南部地區。此外,將前述三種方法之評估結果與近年來該地區地下水硝酸鹽氮實際污染情形進行相關性分析(Pearson及Spearman相關係數),Pearson相關係數分別為0.32、0.39及0.29;Spearman相關係數則分別為0.42、0.39及0.25。再比較三種方法之污染潛勢預測能力,預測高硝酸鹽氮濃度污染(超過0.5mg/L為門檻),其預測能力分別為91%、82%及64%。由上述結果顯示本研究發展以機率為基礎含水層脆弱度評估模式較傳統評估結果更為優異,本研究成果可提供政府劃定地下水污染潛勢範圍以及制訂污染防治與土地使用管理策略之參考。
論文英文摘要:Groundwater is one of the most important water resources. To compare with surface water, groundwater has the advantage of cheap cost, stable temperature and yield, excellent water quality and convenient acquisition. Nowadays, the substantial amount of groundwater has been still extracted to supply agriculture, aquaculture, household and industry needs in many southwestern Taiwan. If groundwater is polluted, the balance of regional water resources supply may be broken and polluted groundwater poses a threat to human health. Therefore, groundwater resources conservation is a very critical issue. The aquifer vulnerability model, DRASTIC, was frequently applied to assess the contamination potential of groundwater. However, because of few observation data on assessed parameters, this model typically involves high levels of prediction uncertainty. This study uses indicator-based geostatistics to develop a probability-based DRASTIC model which is adopted to determine extents of contamination potential and discuss the performance of model prediction. A case study is performed in Cho-shui River alluvial fan. The developed probability-based DRASTIC model includes three methods of parameter estimation – selecting a maximum estimation probability, calculating an expected value and using traditional parameter estimation. The study results reveal that the proximal-fan, which is an agricultural region, is the high contamination potential region, the mid-fan is the medium contamination potential region, and the western coastal and southern areas are the low or no contamination potential region. For selecting a maximum estimation probability, calculating an expected value, and using traditional parameter estimation, the Pearson correlations between the DRASTIC scores and observed nitrate-N concentrations are 0.32, 0.39, and 0.29, respectively, and the Spearman correlations between the DRASTIC scores and observed nitrate-N concentrations are 0.42, 0.39, and 0.25, respectively. To test the predicting performance of high nitrate-N pollution of more than 0.5 mg/L, the accurate prediction rates are 91%, 82%, and 64% for selecting a maximum estimation probability, calculating an expected value, and using traditional parameter estimation, respectively. The analyzed results show that the probability-based DRASTIC model is superior to the traditional one for assessing groundwater vulnerability. The results of this research can provide government administrators with establishing groundwater protection zones and land-use management strategies.
論文目次:中文摘要 I
英文摘要 iii
誌 謝 v
目 錄 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 前言 1
1.2 研究目的 3
1.3 論文架構與流程 3
第二章 文獻回顧 5
2.1 DRASTIC模式文獻回顧 5
2.2 地理統計應用於含水層脆弱度評估 7
2.3 以機率為基礎含水層脆弱度評估 8
第三章 材料與方法 9
3.1 地理位置與水文概況 9
3.1.1 氣候概況 9
3.1.2 河流 9
3.1.3 土地利用類別 11
3.2 濁水溪沖積扇水文地質概述 11
3.3 濁水溪沖積扇地下水氮污染 14
3.4 DRASTIC模式 17
3.5地理統計 26
3.5.1 區域化變數理論 27
3.5.2 半變異元分析 28
3.5.3 半變異元模式 30
3.5.4 指標克利金 31
第四章 結果與討論 36
4.1 DRASTIC各參數等級分布圖 36
4.1.1 地下水深度(D) 38
4.1.2 淨補注量(R) 42
4.1.3 含水層介質(A) 47
4.1.4 土壤介質(S) 52
4.1.5 地形(T) 56
4.1.6 未飽和層影響(I) 57
4.1.7 含水層水力傳導特性(C) 61
4.2 DRASTIC 污染潛勢指標 66
4.2.1 以各參數最大發生機率選取 66
4.2.2 以各參數期望值選取 69
4.3 地下水污染區劃定與硝酸鹽氮污染比較 72
4.4 機率為基礎與傳統DRASTIC模式分析比較 77
4.5 原生權重與特殊權重污染潛勢值之分析比較 83
第五章 結論與建議 86
5.1 結論 86
5.2 建議 87
參考文獻 88
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