Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/90999
標題: 倒傳遞及徑向基底函數類神經網路於流速剖面推估之應用
Application of Back Propagation and Radial Basis Function Artificial Neural Network to Velocity Profile Prediction
作者: 桂宇賢
Yu-Hsien Kuei
關鍵字: back propagation neural network (BPN)
radial basis function neural network (RBFN)
vertical velocity profiles
倒傳遞類神經網路
徑向基底函數類神經網路
平均流速剖面
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摘要: 河川流速量測所獲得資料的正確性,關係到流量估算值的精準度及後續水利工程設計及規劃能否適時達成,並影響災害損失的高低。由於台灣河川先天條件的諸多不確定性,使河川流速量測技術至今仍有許多尚須改善與突破之處,尤其現今洪水災難頻傳加上氣候變遷所導致的極端降雨,使河川流速量測更具挑戰。為避免量測人員長期暴露於危險之環境,可利用大量的量測資料進行平均流速剖面之模擬,以尋求最佳模擬模式,進而獲取更精確的資料,提供設計及規劃使用。 本文旨在探討平均流速剖面之加值分析方法,研究中將以徑向基底函數類神經網路( RBFN )與倒傳遞類神經網路( BPN )進行平均流速剖面模擬之精確度差異分析,並探討影響流速剖面關係建立之因素。研究中應用楊(1998)與林(1999)之實驗資料進行類神經網路之訓練、驗證及測試,並分別藉由相關係數( C.C )與誤差均方根( RMSE )判斷模擬分析與推估之效果。最終將建立合理之類神經網路模式,以實測資料進行平均流速剖面之比對,並提出後續研究之相關建議。
Accuracy of the velocity measurements is related to the accuracy of discharge estimation, the practicality of the project design and planning, and the amount of losses caused by disasters. Because of many uncertainty conditions in Taiwan's rivers, the velocity measuring technique still requires further improvement. In particular, due to the frequent flood disasters caused by the climate change, and the corresponding extreme rainfalls, the river velocity measurement becomes a challenge task. To avoid the exposure to the dangerous environment for the measuring persons, a large number of measured data is used for simulating the average velocity profile and finding the best model for the design and planning. This study aims to compare the accuracy of the radial basis function artificial neural network (RBFN) and back propagation artificial neural network (BPN) for simulating the average velocity profiles. Both Yang (1998) and Lin's (1999) experimental data were adopted for the artificial neural network training, validation and testing. The correlation coefficient (C.C) and the root mean square error (RMSE) were used to determine the effectiveness of the simulation and estimation.
URI: http://hdl.handle.net/11455/90999
其他識別: U0005-2706201416334800
文章公開時間: 2016-07-01
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