在過去針對資料誤差的處理，往往使用常見的統計分析量值來進行誤差處理，但資料的誤差有包含測量造成的誤差，以及預測模型造成的狀態誤差，在針對誤差時往往是結合在一起討論及處理，本研究使用資料同化的概念，將資料透過三維變量分析(3DVAR)以及四維變量分析(4DVAR)的代價函數（Cost Function）的建立，釐清在資料處理中，資料本身的測量誤差以及透過該物理量建立的數學模型中演算法內參數對於資料誤差的個別影響，並透過集成卡爾曼濾波器（Ensemble Kalman Filter）的計算，將資料進行過濾分析，並觀察資料的收斂情形。
資料同化系統可以使用在各個營建工程領域的應用，在本研究將使用自來水一字單管段，由文獻回顧創建一個實驗設計場域，藉由觀察管內的流態以及透過水力軟體的計算以及數學最佳化演算法的幫助，提供給資料同化系統帶有混合誤差的資料，並且針對實驗設計理面的參變數造成的差異進行評估，使用發展的集成卡爾曼濾波器（Ensemble Kalman Filter）來得到優化後的資料，並且使用基於3DVAR以及4DVAR的邏輯之EnKF來評估誤差收斂。發現兩者實際上可以同時處理測量誤差以及模型誤差。

In the past, statistical analysis was used to deal with data errors. However, data errors include measurement and state errors caused by prediction models. To deal with both errors simultaneously, this study applies the concept of data assimilation, in which three-dimensional variable analysis (3DVAR) and four-dimensional variable analysis (4DVAR) are available. Both can clarify the measurement error of the data itself and the analytical model. By the calculation of Ensemble Kalman Filter (EnKF), it is observed that data error generally converges.
In this study, a simulation case of pipe segment was generated. With the use of the hydraulic software and the optimization algorithm, the output with mixed both errors (measurement error, model error) are provided to the data assimilation model. EnKF was used to evaluate the error convergence based upon the logic of 3DVAR and 4DVAR. It is found that both can practically deal with the measurement and state errors at the same time.

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