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| 研究生： |
陳厚元 Chen, Hou-Yuan |
|---|---|
| 論文名稱： |
小波及灰色理論應用於地層下陷預警之研究-以濁水溪沖積扇為例 The Application of Wavelet and Grey Theory in the Land-Subsidence Potential analysis for the Cho-Shui River Fan |
| 指導教授： |
呂珍謀
Leu, Jan-Mou |
| 學位類別： |
碩士 Master |
| 系所名稱： |
工學院 - 水利及海洋工程學系碩士在職專班 Department of Hydraulic & Ocean Engineering (on the job class) |
| 論文出版年： | 2010 |
| 畢業學年度： | 98 |
| 語文別： | 中文 |
| 論文頁數： | 172 |
| 中文關鍵詞： | 灰關聯分析 、小波分析 、奇異點 、灰色預測模型 |
| 外文關鍵詞： | Grey Relational Analysis, Wavelet Analysis, Singularity, Grey prediction model |
| 相關次數： | 點閱：137 下載：3 |
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本文以各層地下水位和沉陷量的相關性著眼，利用地下水位站普遍性設立及各種時水位至日水位頻率量測值皆具之優點，考量地下水位下降與地層下陷之相關性，最多包含第一~第五含水層之水位下降率，而利用相對於各地下水位站時間和空間之沉陷量內插值，以合理推論出關鍵地下水位站及關鍵含水層之水位變化。但地下水位站為數不少，為減低數據處理之難度及聚焦於非關鍵之地層下陷中心等影響，進行以下分析:(1)先以由5年至28年累積下陷及地層下陷中心分析方法，選出關鍵地下水位站。(2)選出可代表之地下水位站，再以應用灰關聯分析法求出相對於地層下陷量之含水層(1-5層)之水位下降率，進而淬取影響沉陷量的關鍵含水層。(3)淬取後之各層或單層地下水位，則依據地下水位長期變動趨勢分析結果，並依水位空間變化劇烈程度，選取多處觀測井進行指標水井管理水位分析。(4)進而以小波分析方法地下水位之時序值，從地下水位之動態變化的歷史來分析局部奇異點振幅大小及時間點。(5)以灰色預測模型GM(1,1)預測其地層下陷潛勢，求出預測值。(6)再結合局部奇異值振幅及時間點等參考變數，藉以設定地陷潛勢預警地下水位。
由地下水位和沉陷關係，可以觀察出小波分析最大振幅發生時間點，延後平均2年內即發生年最大沉陷量，且準確率可達85%以上，而出現小波分析無法辨識解讀情況者，都集中於雲林沿海，則為沿海地區潮汐或人為開發之影響所導致。
This thesis focuses on the relationship between different layers of groundwater levels and the settlement amount. I utilize the advantages of frequently settled groundwater station and each hourly and daily water level frequency measurements, consider the relationship between groundwater level drawdown and land subsidence, including the descending rate of water level from the first to fifth aquifer, and utilize each groundwater station’s spatiotemporal related interpolate value of settlement amount to reasonably conclude the critical groundwater station and the critical changes of water level in aquifer. However, there are lots of groundwater stations. Hence, in order to decrease the difficulties for handling the statistics and the influences from mistakenly focusing on non-critical land subsidence centers, I did the following analysis: First, I chose the critical groundwater stations from analyzing the data of the land subsidence centers and other descending areas which have accumulated from five to 28 years. Second, I selected representative groundwater stations and applied to the grey relational analysis to figure out the related amount of land subsidence to the descending rate of water level (first to fifth) in order to extract the most critical aquifer which influences the settlement amount. Third, I analyzed the results of the extracted multi- or mono- groundwater levels according to the long-term changing tendencies of ground water level. Moreover, I selected many observation wells to conduct the indicated well management water level analysis based on the severe degree of water level spatial fluctuations. Fourth, I utilized the timing value of groundwater level of wavelet analysis and the history of movement fluctuations of ground water levels to analyze the partial singularity amounts of amplitude of vibrations and time period. Fifth, I used grey prediction model GM (1,1) to predict the potential development of land subsidence and figure out a predicted value. Sixth, I combined the reference variables from partial singularity amounts of amplitude of vibrations and time period to establish the potential development of subsidence in order to warn the groundwater levels earlier.
From the relationship between groundwater level and settlement amounts, we can observe the biggest amplitude time period from wavelet analysis and delay, on average, the biggest settlement amount in two years. Additionally, the accuracy rate can be over 85%. The areas that can not be identified by wavelet analysis are mostly concentrated in coastal regions of Yun-Lin, which is influenced due to the tidal intrusions and artificial developments.
1.丁崇峯(2006)，機器學習演算法應用於地下水位與地層下陷量分析之研究，國立成功大學水利及海洋工程所博士論文。
2.中興工程顧問公司(2002)，台灣地區地下水資源管理決策支援系統建置(2/4)，經濟部水資源局。
3.王文聖、丁晶、李躍清 (2005) ，水文小波分析，化學工業出版社。
4.江亮 (2002)，灰色GM(1,1)模型在經濟預測中的應用， 哈爾濱工業大學實驗學院。
5.李工農、阮曉青、徐晨 (2007) ， 21世紀經濟學教材：經濟預測與決策及其Matlab 實現，清華大學。
6.李弼程、羅建書(2003)，小波分析及其應用，機械工業。
7.周孟科(1999)，灰色理論應用於地層下陷之預測，國立成功大學水利及海洋工程研究所碩士論文。
8.財團法人工業技術研究院能資所(2001~2008)，『台灣地區地層下陷之監測、調查及分析』，經濟部水利署。
9.財團法人成大研究發展基金會 (2007-2008)，地層下陷潛勢分析與警示計畫，經濟部水利署。
10.國立成功大學水工試驗所(2003)，台灣地區地下水資源圖說明書，經濟部水利署。
11.國立成功大學水工試驗所(2005)，台灣地區地下水文圖圖集繪工作，經濟部水利署。
12.國立成功大學水工試驗所(2005-2008)，地層下陷防治服務團年度工作計畫報告，經濟部水利署。
13.張善文、雷英傑、馮有前 (2007)，MATLAB在時間序列分析中的應用，西安電子科技大學。
14.張新紅(2008)，小波網絡理論及其在經濟建模中的應用，東北財經大學。
16.張德豐(2009)，MATLAB小波分析，機械工業。
17.曹凱、許昌(2000)，灰色理論GM(1，1)、GM(1，N)聯合模型在建築物沉降預測中的應用，水科學與工程技術，p54-57。
18.許乃文(2000)，灰色理論及類神經網路應用於雲林地區地層下陷之研究，國立成功大學土木工程研究所碩士論文。
19.陳文福(2005)，台灣的地下水，遠足文化事業公司。
20.陳永光(2008) ，電子信息裝備試驗灰色系統理論運用技術， 國防工業。
21.陳厚元、洪銘堅、曾鈞敏、許榮庭、丁崇峰 (2008)，地層下陷警示系統規劃建置，第30屆海洋工程研討會特刊-特別議題，p81-89。
22.溫坤禮(2002)，灰預測原理與應用，全華圖書公司。
23.鄧聚龍(2002)，灰預測與灰決策，華中科技大學。
24.Donoho D., "Nonlinear Wavelet Methods for Recovery of Signals, Densities, and Spectra from Indirect and Noisy Data," Different Perspectives on Wavelets, Proceeding of Symposia in Applied Mathematics, Vol 47, I. Daubechies ed. Amer. Math. Soc., Providence, R.I., 1993, p 173-205.。
25.Daubechies I., "Orthonormal Bases of Compactly Supported Wavelets," Comm. Pure Appl. Math., Vol 41, 1988, p 906-966.。
26.Bradley J., C. Brislawn, and T. Hopper, "The FBI Wavelet/Scalar Quantization Standard for Gray-scale Fingerprint Image Compression," Tech. Report LA-UR-93-1659, Los Alamos Nat'l Lab, Los Alamos, N.M. 1993.。
27. Cody M.A., "The Wavelet Packet Transform," Dr. Dobb's Journal, Vol 19, Apr. 1994, p 44-46, 50-54.。
28.Wickerhauser M.V., "Acoustic Signal Compression with Wave Packets," 1989. Available by TeXing this TeX Paper.。
29. Wickerhauser M.V., Adapted Wavelet Analysis from Theory to Software, AK Peters, Boston, 1994, p 213-214, 237, 273-274, 387.。
30. Meyer Y., Wavelets: Algorithms and Applications, Society for Industrial and Applied Mathematics, Philadelphia, 1993, p13-31, 101-105.。
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