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研究生:許程翔
論文名稱:主成分分析在烏溪流域水質評價之應用
論文名稱(外文):Application of Principal Component Analysis of Water Quality Assessment in Wu River
指導教授:李漢鏗李漢鏗引用關係
指導教授(外文):Lee Han-Keng
口試委員:童翔新葉怡嚴
口試日期:2014-07-04
學位類別:碩士
校院名稱:逢甲大學
系所名稱:水利工程與資源保育學系
學門:工程學門
學類:河海工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:165
中文關鍵詞:主成份分析水質評價河川水質
外文關鍵詞:principal component analysiswater quality evaluationriver water quality
相關次數:
  • 被引用被引用:8
  • 點閱點閱:750
  • 評分評分:
  • 下載下載:231
  • 收藏至我的研究室書目清單書目收藏:1
摘要
環境水體監測係藉由採樣、檢測、統計分析以瞭解水體水質現狀,透過水體評價指標對水體進行評估與分級,提供政府在水污染防治上的依據。影響環境水體水質的污染源眾多,傳統以水質標準或水質綜合指標分析水體水質的變異。目前研究中,常利用多變量分析探討環境水體之污染成因與水質特性。本研究以多變量分析之主成份分析、因子分析及群集分析,探討烏溪流域水質污染成因及分佈特性。
本文以烏溪流域的水質監測資料做為研究樣本,水質數據經由完整性篩選後,決定以2005-2013年間,水溫、酸鹼值、電導度、溶氧、生化需氧量、化學需氧量、懸浮固體、大腸桿菌、氨氮等9項水質項目為研究分析樣本。數據經過ND值處理及遺漏值補遺後,主成份分析之適合度檢定(KMO值)從0.663升至0.683;其第一及第二主成份的合計解釋變異量從49%升至51%。當水質資料的單位不一致時,需以相關性矩陣進行運算。將標準化與未標準化的數據分別進行主成份分析,顯示在KMO值、特徵值及主成份方程式的結果均一樣。
研究結果顯示,烏溪流域在主成份分析中,影響水質變異的項目以BOD、COD及氨氮為主,水溫及電導度為次。藉由因子分析可顯示烏溪水體污染的成因為「有機污染因子」及「環境因子」;以群集分析可依污染程度分為四個群集。藉由主成份分析綜合評價烏溪水質,結果與RPI、WQI5的評價有部分差異;但主成份分析包含多項水質項目,能夠完整呈現水質污染的特性。
Abstract
The monitoring of environmental water bodies involves examining the current quality of water bodies by sampling, testing, conducting statistical analysis, assessing and classifying water bodies by using water evaluation indices, and providing the government with references regarding water pollution control. Multiple sources of pollution influence the quality of environmental water bodies, and traditionally, water quality standards or comprehensive water quality indices are adopted for analyzing the variations in the quality of water bodies. Current studies have frequently adopted multivariate analysis to analyze and investigate the causes of pollution and the water quality characteristics of environmental water bodies. This study adopted the methods of multivariate analysis such as principal component analysis, factor analysis, and cluster analysis to explore the causes and distributions of water pollution in the Wu River Basin.
This study used the water-quality monitoring data of the Wu River Basin as research samples. After selecting the water quality data based on data completeness, 9 water quality evaluation indicators, measured between 2005 and 2013, were adopted in this study as the samples for research analysis: water temperature, pH, conductivity, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, E. coli, and ammonia-nitrogen. After using non-detected values to process the data and imputing missing values, the goodness-of-fit test result (Kaiser-Meyer-Olkin value) of principal component analysis increased from 0.663 to 0.683. The total variance that the first and second principal components accounted for increased from 49% to 51%. When water-quality data were provided by distinct units, the data must be calculated by using a correlation matrix, and standardized and nonstandardized data were individually analyzed using principal component analysis. The results indicated that both types of data produced identical results regarding Kaiser-Meyer-Olkin values, eigenvalues, and principal component equations.
The research results indicated that in the principal component analysis of the Wu River Basin, the major indicators that affected water-quality variation were biochemical oxygen demand, chemical oxygen demand, and ammonia-nitrogen; the minor indicators included water temperature and conductivity. The results of factor analysis revealed that organic pollution factors and environmental factors caused the pollution in the Wu River water body; the pollution was grouped into four clusters based on the levels of population by using cluster analysis. The water quality of the Wu River was comprehensively assessed by using principal component analysis. These results slightly differed from those obtained in evaluations using the river pollution index(RPI) and the water quality index 5(WQI5). Because principal component analysis involves various water-quality evaluation indicators, it can be used to present the characteristics of water pollution comprehensively.
目錄
摘要............... I
目錄 ..........................................................................................................V
圖目錄 IX
表目錄 XI
第一章 緒論 1
1-1前言 1
1-2 研究目的 1
1-3 研究方法 2
第二章文獻回顧 5
2-1國內研究現況 5
2-2多變量分析 7
2-3 主成份分析 8
2-3-1 主成份分析狀況 8
2-3-2 主成份選取原則 12
2-4因子分析 14
2-5群集分析 15
2-6水質評估 17
第三章研究方法 19
3-1流域概況 19
3-2河川水質評方法 23
3-2-1 水質評價 23
3-2-2 烏溪河川水體分類 26
3-3多變量分析方法與原理 28
3-3-1相關分析 29
3-3-2主成份分析 31
3-3-3因子分析 33
3-3-4群集分析 39
3-4資料處理 41
第四章結果與討論 45
4-1數據篩選 45
4-1-1數據完整度篩選 45
4-1-2 水質概況 48
4-1-3 水質項目篩選 52
4-2數據處理 54
4-2-1數據補遺 55
4-2-2 ND值處理 56
4-3檢定分析 57
4-4相關性檢定 65
4-5主成份分析 71
4-5-1數據分析前處理 71
4-5-2主成份萃取 81
4-5-3因子負荷圖(主成份分析) 86
4-5-4因子負荷表 94
4-5-5因子得分圖 103
4-6因子分析 108
4-6-1 因子萃取 108
4-6-2 因子負荷(因子分析) 113
4-7群集分析 118
4-8水質評價指標 122
第五章 結論與建議 129
5-1結論 129
5-2 建議 130
參考文獻 131
附錄一 遺漏值 141
附錄二 ND值 149
附錄三 BOX圖 157
附錄四 國內27篇參考文獻 161
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