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題 名 | NETSTARS 模式參數最佳化之研究=Parameter Optimization for NETSTARS |
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作 者 | 謝慧民; 楊音琳; | 書刊名 | 水土保持學報 |
卷 期 | 45:2 2013.06[民102.06] |
頁 次 | 頁617-640 |
分類號 | 448.6 |
關鍵詞 | 倒傳遞類神經網路; NETSTARS 模式; 參數最佳化; Back-propagation neural network; Network of stream tube model for alluvial river simulation; NETSTARS; Parameter optimization; |
語 文 | 中文(Chinese) |
中文摘要 | 本研究以離散參數試誤法、人工經驗調整法與倒傳遞類神經網路法,優選NETSTARS 模 式參數,並評估其成效。所用參數為河道曼寧n 值及可沖刷厚度參數Alt 值,模擬對應的成果 分別為水位歷程及河床縱斷面高程變化。第一法的推估成果被當成近似理論解,做為評估標準。 由水位變動成果發現,以倒傳遞類神經網路法與離散參數試誤法得到的曼寧n 值較為一致,平 均值也與人工經驗調整法接近,均適用於NETSTARS 模式,但不同事件所得之最佳參數值仍 有些許差異;在河床變動成果的部分,後兩法成果均與第一法差異頗大,由於Alt 值無法由最 佳化方法獲得相近的參數成果,因此這些最佳化方法均不適用於此參數之推估。 |
英文摘要 | Methods, such as the experience-based artificial adjustment, back-propagation neural network, and discrete-parameter trial-and-error, were used to investigate the optimal performances of these parameters of the NETSTARS model. The parameters adopted in this study include Manning's n value of channels and Alt value of scouring thickness, and the corresponding results are water level hydrograph and longitudinal riverbed profile. The results of the discrete-parameter trial-and-error method are regarded as approximate theoretical solutions, and it is regarded as a evaluation criteria. The simulation regarding water level change reveals that the Manning's n values resulting from the back-propagation neural network and thediscrete-parameter trial-and-error method show the consistency, and the average of these values is also close to the result of experience-based artificial adjustment method. So, those optimization methods are suitable to NETSTRAS model for Manning's n estimation, but the optimized parameters show the significant discrepancy in different events. For the riverbed change, the results of the last two methods vary considerably with the result of the first method. Because similar Alt values cannot be obtained by the optimization methods, these methods are not applicable to this parameter’s estimation. |
本系統中英文摘要資訊取自各篇刊載內容。