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系統識別號 U0026-0812200915081768
論文名稱(中文) 利用機器學習修正QPESUMS雷達估計降雨
論文名稱(英文) Correction of QPESUMS Radar Rainfall Using Machine Learning Methods
校院名稱 成功大學
系所名稱(中) 水利及海洋工程學系碩博士班
系所名稱(英) Department of Hydraulics & Ocean Engineering
學年度 97
學期 2
出版年 98
研究生(中文) 劉鑌鋈
研究生(英文) Bin-wu Liu
電子信箱 N8696406@ncku.edu.tw
學號 n8696406
學位類別 碩士
語文別 中文
論文頁數 102頁
口試委員 口試委員-張斐章
口試委員-童慶斌
口試委員-張良正
口試委員-呂珍謀
指導教授-游保杉
中文關鍵字 雷達估計雨量  地面雨量站  QPESUMS  支撐向量回歸(SVR)  輻狀基底函數類神經網路(RBFNN) 
英文關鍵字 QPESUMS  Radial basis function neural network  Raingauge  Support vector regression  Radar rainfall estimation 
學科別分類
中文摘要 本研究利用中央氣象局QPESUMS (quantitative precipitation estimation and segregation using multiple sensors)系統所提供之雷達估計雨量,以支撐向量回歸(support vector regression, SVR)及輻狀基底函數類神經網路(radial basis function neural network, RBFNN)兩種模式,結合地面觀測雨量及地理空間因子進行修正雷達估計雨量,其目的為期望提昇雷達估計降雨之準確性,並保有雷達雨量具高解析度及涵蓋完整區域之優點。本研究選取七場颱風事件作為模式率定與驗證,其中前四場事件作為模式之率定,後三場事件為驗證,以地面雨量站對應上方九個雷達估計雨量、地面雨量站位置之X 坐標與Y 坐標、地面雨量站高程以及地面雨量站與七股雷達站的直線距離為四種輸入因子,依不同因子的組合分別建立五種輸入向量,並討論不同的輸入向量對於模式建立之影響程度。經由分析結果可知隨著影響降雨特性之輸入因子資訊量增加,兩種模式的率定結果越好;本文以2007 年三場颱風事件進行台灣西南部之時雨量修,初步結果顯示兩種模式表現在總雨量方面,能將原雷達估計雨量之均方根誤差減少將近一半,且其相關係數能由0.7 提昇至0.8 以上,顯示SVR 模式以及RBFNN模式能夠有效利用地面觀測雨量訊息結合雷達估計雨量,尤其在山谷地區及靠近中央山脈之雷達估計降雨之修正結果,支撐向量回歸(SVR)比輻狀基底函數類神經網路(RBFNN)能有效地掌握雷達雨量於地理空間因子之相關訊息。
英文摘要 This study developed two machine learning methods, the support vector regression (SVR) and the radial basis function neural network (RBFNN), to improve radar rainfall from the high resolution QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provided by the Central Weather Bureau in Taiwan. The goal of this research is to enhance the accuracy of radar rainfall estimation which are found largely underestimate in the mountain regions, but to preserve the advantage of high spatial and temporal resolution of radar rainfall. Various input vectors of two machine learning models were combinations of predictors consisting of the nine radar estimated rainfalls centered at the raingauge location, the coordinates of raingauge positions, the elevation of raingauge, and the distance between Chi-Guw weather radar station and the raingauge. This work found that the model using more predictors in the input vector performs better than that using less predictors. Seven typhoon rainfall events in the southwest Taiwan were used as calibration and validation data in this work. Analytic results show that the SVR and the RBFNN have the capability to adjust radar estimated rainfall, from the evidence that the root mean square error was reduced and the correlation coefficient increased. Moreover, the SVR outperformed the RBFNN, especially in the valley and in the mountain regions.
論文目次 中文摘要 I
英文摘要 II
誌 謝 III
目 錄 IV
表 目 錄 VII
圖 目 錄 VIII
第一章 緒論 1
1-1 研究動機與目的 1
1-2 文獻回顧 2
1-2-1 雷達回波資料處理 2
1-2-2 雨量內插法 4
1-2-3 回歸模型估計法 6
1-3 本文組織 9
第二章 氣象雷達雨量估計方法 12
2-1 氣象雷達觀測原理 12
2-2 估計降水量方法 15
2-3 QPESUMS系統介紹 16
2-3-1 功能簡介 16
2-3-2 產品資訊 17
第三章 模式介紹 22
3-1 支撐向量機(SVM) 22
3-1-1 結構風險最小化 22
3-1-2 支撐向量回歸(SVR) 24
3-1-3 SVR之參數率定 30
3-2 輻狀基底函數類神經網路(RBFNN) 30
3-2-1 RBFNN網路架構 31
3-2-2 RBFNN學習演算法 34
第四章 研究區域與資料分析 36
4-1 研究區域 36
4-2 QPESUMS網格繪製 39
4-3 資料收集 41
4-4 降雨空間分布比較 47
4-4-1 單點雨量比較 47
4-4-2 雨量與地面雨量站高程比較 51
4-4-3 雨量與七股雷達站距離比較 55
第五章 雷達估計雨量之修正 61
5-1 輸入因子選擇與資料之前置處理流程 61
5-1-1 輸入因子選擇與組合 61
5-1-2 資料之前置處理流程 64
5-2 模式率定結果 65
5-2-1 模式率定方法與評鑑指標 65
5-2-2 SVR之率定結果 66
5-2-3 RBFNN之率定結果 71
5-3 SVR與RBFNN模式驗證結果 76
5-3-1 時雨量之驗證結果 76
5-3-2 雨量誤差之高程分布 84
5-3-3 雨量誤差之空間分布 88
第六章 結論與建議 94
6-1 結論 94
6-2 建議 96
參考文獻 97
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