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博碩士論文 etd-0830110-151354 詳細資訊
Title page for etd-0830110-151354
論文名稱
Title
以類神經網路模擬暴浪型海灘剖面特徵
Application of Neural network to characterize a storm beach profile
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
135
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2010-06-18
繳交日期
Date of Submission
2010-08-30
關鍵字
Keywords
暴浪型海灘、倒傳遞類神經網路
storm beach, back-propagation neural network
統計
Statistics
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中文摘要
台灣四面環海,地狹人稠,在陸地有限的情況下,防護既有海岸或創造安定的人工海灘都是值得考量的策略。當颱風過境時,海灘受到颱風暴潮巨浪的影響,導致灘線後退,同時被侵蝕的海灘沙土則被帶到碎波帶之外形成離岸潛洲。颱風過後,因餘湧及季節性波浪將潛洲沙土逐漸向陸推移,使原有海灘可能逐漸回復;在這種機制下,海灘剖面亦可能形成一個自然的動態平衡。
本研究之主要目的在應用具有學習演算及非線性最佳化優點的類神經網路中的倒傳遞類神經網路,利用一組範例(即系統輸入及輸出所組成的資料)建立系統模型(輸出入之間的關係),用以推估、預測、決策及驗證。藉由倒傳遞網路模式,可模擬颱風暴浪侵襲時的海灘剖面特徵的變化,並模擬驗證美國工程師兵團CERC在1960年代及日本中央電力工業研究所在1980年代於大型波浪水槽所得之海灘剖面實驗結果,再針對海灘剖面受颱風暴浪作用所造成的剖面變化,尤其是濱台的侵蝕及潛洲的形成,進行分析探討。最後經由文獻蒐集,並模擬海灘剖面之相關參數,進行剖面形狀的預測,建立暴浪型海灘之預測模式。
Abstract
Taiwan is a small island state surrounded by the oceans but with large population. With limited land space, it would be worthwhile considering how to stabilize the existing coast or to create stable artificial beaches. Under the onslaught of storm surge and large wave from typhoons, beach erosion would occur accompanying by formation of a submerged bar beyond the surf zone with the sand removed from the beach. After the storm, the bar material maybe transport back by the swell and predominant waves which helps recover the original beach, thus producing a beach profile in dynamic equilibrium.
The main purpose of this research is to use the back-propagation neural network(BPNN), which trains a sample model and creates a system for the estimation, prediction, decision making and verification of an anticipated event. By the BPNN, we can simulate the key characteristic parameters for the storm beach profile resulting from typhoon action. Source data for training and verification are taken from the experimental results of beach profile change observed in large-scale wave tank(LWT)conducted by Coastal Engineering Research Center(CERC)in the USA in the 1960s and that from the Central Research Institute of Electric Power Industry in Japan in the 1980s. Some of the data are used as training pairs and others for verification and prediction of the key parameters of berm erosion and bar formation. Through literature review and simulation on the related parameters for storm beach profile, methodology for the prediction of the beach profile and bar/berm characteristics can be established.
目次 Table of Contents
目錄
誌謝 I
中文摘要 III
英文摘要 IV
目錄 V
圖目錄 VIII
表目錄 XIII
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 研究方法 9
1.4 本文研究組織 9
第二章 暴浪型海灘剖面特徵 11
2.1 海灘剖面分類 11
2.1.1一般型海灘剖面 17
2.1.2暴浪型海灘剖面 22
2.2 暴浪型海灘剖面特徵 23
2.2.1暴浪型海灘剖面特徵參數 26
2.2.2 Bar與berm的界定 28
第三章 類神經網路系統 31
3.1 類神經網路簡介 31
3.1.1類神經網路定義 32
3.1.2類神經網路架構 34
3.2 倒傳遞類神經網路簡介 37
3.2.1 倒傳遞網路的架構 37
3.2.2 倒傳遞網路演算法 38
3.2.3 倒傳遞網路的參數 46
3.2.4 倒傳遞類神經網路的應用流程 47
第四章 類神經網路在暴浪型海灘的應用 48
4.1 暴浪型海灘資料彙整 48
4.1.1 訓練資料來源 52
4.1.2 海灘剖面特徵輸入值 57
4.2 暴浪型海灘各參數預測 59
4.2.1 網路模式架構及最佳化 60
4.2.2暴浪型海灘剖面地形各參數預測結果 70
4.3 動態暴浪型海灘剖面之模擬 75
4.3.1 海灘動態地形剖面特徵參數前處理 76
4.3.2 倒傳遞類神經網路模式最佳化的格點選擇 86
4.3.3 網路預測結果及分析 98
4.4 現場海灘剖面驗證 105
4.4.1 驗證資料 106
4.4.2 驗證結果與分析 109
第五章 結論與建議 111
5.1 結論 111
5.2 建議 112
參考文獻 參1

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