博碩士論文 976204012 詳細資訊




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姓名 林淑惠(Shu-hui Lin)  查詢紙本館藏   畢業系所 應用地質研究所
論文名稱 以超曲面迴歸克利金進行降雨量空間推估
(Rainfall Spatial Interpolation Using Hypersurface Regression Kriging)
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摘要(中) 豪雨誘發山崩之研究中,降雨量為相當重要的促崩因子。欲得知空間中任一點的降雨量最直接的方法即為從雨量站觀測求得。惟架設較密的雨量站須耗費大量金錢和人力資源,所以為求得規則網格點雨量值,曾有許多不同的內插方法被用來推估降雨量的空間分佈。本研究使用迴歸克利金(regression kriging,RK),並與其他既有內插方法互相比較,以瞭解新方法之有效性。
本研究以2005年馬莎颱風事件為例,收集水利署及氣象局在馬莎颱風期間之雨量站資料,經檢視與校正後將資料按測站整理成最大時雨量以及總雨量,再進行石門水庫集水區降雨量空間推估。研究中分別以單變量的距離平方反比法與克利金法,以及結合數值高程模型的RK_1D、RK_trend兩種多變量地質統計方法進行降雨空間內插。RK_1D係使用降雨量與高程的一維線性迴歸式做為推估降雨量的趨勢,但台灣地區降雨量與高程之相關係數偏低(0.26),故加入高程值進行推估未能有效改善降雨量分佈推估。為改善此問題,RK_trend使用雨量站座標及高程值擬合出降雨量之超曲面,進行分析後發現其推估結果較其他方法更能表現出細微的空間降雨量分佈,且其估計誤差也較小,表示RK_trend的推估結果較佳。
本研究並針對四種不同內插方法分別進行交叉驗證,結果證實各種地質統計方法的估計誤差值確實具有可信度,尤以最大時雨量的驗證結果為然。雖然以克利金所得的交叉驗證結果整體誤差最小,但詳細探討後發現若兩鄰近雨量站的高程與降雨量相近時,RK_trend的交叉驗證所得真實誤差最小,較無偏估的現象。
摘要(英) In storm-induced landslide study, rainfall capacity is an important triggering factor. A direct way to know rainfall value at any point in the study area is interpolation from the rainfall observation gauges. Because the density of gauge stations is commonly inadequate, many different interpolation methods were used for estimate the spatial distribution of rainfall. In this study, we test regression kriging (RK), and compare the effectiveness of this new method with other existing interpolation methods.
In this study, we collect rainfall data during the typhoon Matsa from the Water Resources Agency, Taiwan and from the Central Weather Bureau, Taiwan. These data were visually examined and errors were fixed. Good quality data were processed to extract total rainfall and maximum hourly rainfall values. Inverse square distance method and kriging method were used for comparison with two multivariate geostatistical algorithms: RK_1D and RK_trend. RK system uses the rainfall value as primary variable and the elevation as auxiliary variable. Because the rainfall values and the elevations have a correlation coefficient only about 0.26, the auxiliary variable cannot effectively improve the rainfall estimation in RK_1D. To solve this problem, we tested RK_trend. A hypersurface which incorporates locations (x, y) and elevation (z) was used to describe the drift of rainfall values. The results find that the RK_trend method shows better rainfall spatial distribution and smaller estimation errors than that of other methods.
The performances of the four interpolators were further examined by cross-validation method. Results confirm that the errors estimated from various geostatistical methods do have reliability, especially for the maximum hourly rainfall case. Although cross-validation result indicates kriging method provides the smallest mean absolute error, however when two rain gauge stations are close, and the rainfall values as well as the elevations are similar, RK_trend method provides the smallest mean absolute error and indicates less bias.
關鍵字(中) ★ 內插
★ 地質統計
★ 迴歸克利金
★ 降雨
關鍵字(英) ★ interpolated
★ rainfall
★ geostatistics
★ regression kriging
論文目次 中文摘要 I
英文摘要 III
致 謝 V
目 錄 VI
圖 目 X
表 目 XIV
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.3 研究架構與流程 5
第二章 研究方法 9
2.1 地質統計學簡介 9
2.2 區域化變數理論 9
2.2.1 區域化變數 9
2.2.2 結構分析 10
2.3 一般克利金法 13
2.4 迴歸克利金 16
2.4.1 迴歸克利金(1D) 16
2.4.2 迴歸克利金(trend) 18
2.5 距離平方反比法 19
2.6 交叉驗證 20
2.7 定常性分析 20
第三章 資料蒐集與處理 23
3.1 研究區概述 23
3.2 資料蒐集 23
3.2.1 馬莎颱風簡介 23
3.2.2 雨量資料 25
3.3 資料處理 28
3.3.1 雨量資料檢核 28
3.3.1 雨量資料處理 31
3.3.2 高程資料處理 31
第四章 馬莎颱風總雨量空間推估及驗證 33
4.1 總雨量基本資料統計 33
4.2 總雨量次數分佈圖 33
4.3 總雨量資料空間分析 37
4.4 總雨量推估 40
4.4.1 距離平方反比法推估結果 40
4.4.2 克利金推估結果 41
4.4.3 迴歸克利金(1D)推估結果 43
4.4.4 迴歸克利金(trend)推估結果 46
4.4.5 各種不同方法推估結果比較 49
4.5 總雨量交叉驗證 50
4.5.1 驗證結果 50
4.5.2 理論估計誤差分佈之驗證結果 51
4.5.3 各方法平均絕對誤差之探討 56
第五章 馬莎颱風最大時雨量空間推估及驗證 59
5.1 最大時雨量基本資料統計 59
5.2 最大時雨量次數分佈圖 59
5.3 最大時雨量資料空間分析 62
5.4 最大時雨量推估 65
5.4.1 距離平方反比法推估結果 65
5.4.2 克利金推估結果 66
5.4.3 迴歸克利金(1D)推估結果 67
5.4.4 迴歸克利金(trend)推估結果 69
5.4.5 各種不同方法推估結果比較 71
5.5 最大時雨量交叉驗證 72
5.5.1 驗證結果 72
5.5.2 理論估計誤差分佈之驗證結果 72
5.5.3 各方法平均絕對誤差之探討 72
5.6 總雨量與最大時雨量結果比較 73
第六章 討論 79
6.1 地形因子平滑化範圍之影響 79
6.2 迴歸克利金(trend)的降雨超曲面階數選取 86
6.2.1 殘差次數分佈圖 87
6.2.2 殘差半變異圖 87
6.2.3 定常性分析 92
6.3 內插結果探討 97
6.3.1 內插結果空間分布 97
6.3.2 估計誤差 98
6.4 驗證結果探討 98
6.5 雨量推估結果與山崩研究 104
第七章 結論與建議 109
7.1 結論 109
7.2 建議 110
參考文獻 113
附錄一 本研究使用雨量站基本資料 119
附錄二 定常性分析結果 125
附錄三 馬莎颱風總雨量迴歸克利金(trend)各階推估結果 139
附錄四 馬莎颱風最大時雨量迴歸克利金(trend)各階推估結果 151
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指導教授 李錫堤(Chyi-tyi Lee) 審核日期 2010-7-29
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