結合資料探勘方法建立屏東平原含水層水文地質參數推估模式

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林永清(Yong-Qing Lin) 查詢紙本館藏

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土木工程學系

論文名稱

結合資料探勘方法建立屏東平原含水層水文地質參數推估模式
(Spatial Estimation of Hydrogeological Parameters by Using Data Mining Methods in Pingtung Plain Aquifer)

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摘要(中)

面對未來可能發生的缺水危機，需要對水文地質條件與地下水流動特性有著充分瞭解。水文地質參數如滲透係數及蓄水係數等，乃是地下水數值模擬及水資源管理中必不可少的關鍵資料，但傳統上水文地質參數乃是通過現地抽水實驗的方式獲取，在空間解析度方面存在一定限制。
因此，本研究以時空資料探勘方法—擴展經驗正交函數法(Extended Empirical Orthogonal Function, EEOF)為資料分析基礎結合機器學習模型，以屏東平原為例，建立水文參數推估模式。本研究首先收集了研究區域?地下水水位資料與其他水文資料，透過EEOF分解出時空特徵，並利用其空間特徵結合其他參數建立機器學習模型來推估蓄水係數及滲透係數。結果顯示地下水位進行EEOF分解後，首先可很好提供降雨與地下水之間的時空變異特性資訊；其次，地下水位空間特徵在接下來的水文地質參數推估中，能夠提供非常重要的訊息。最後的模式結果證明，結合了地下水位空間特徵的水文地質參數推估模式所得到結果的準確率較高，期望能為未來臺灣各地區地下水研究提供參考。

摘要(英)

To face the likelihood and severity of water shortage, understanding the hydrogeological conditions and groundwater flow are important. Hydrogeological parameters of aquifer such as hydraulic conductivity (K) and storage coefficient (S) are the essential and crucial basic data in the groundwater modeling and resource assessment. However, traditionally, the estimation of hydrogeological parameters is inefficient in spatial resolution, time consuming and expensive from pumping test.
In this study, a data mining framework based on Extended Empirical Orthogonal Function (EEOF) combing with Machine Learning models were applied to estimate the hydraulic conductivity and storage coefficient. We extract the major spatial-temporal patterns of groundwater level variation from EEOF and use them to build a spatial machine learning model to estimate the hydrogeological parameters in Pingtung plain. The EEOF results have shown that this analysis framework can provide the information of main variation of spatial temporal feature between rainfall and groundwater, and it can provide crucial information for the machine learning model. The model results have shown that the model precision is quite high. This framework could also apply to other aquifers and provide as a very useful information for groundwater modeling and management through the pure data-driven techniques proposed by this study.

關鍵字(中)

★ 資料探勘
★ 擴展經驗正交函數
★ 地下水流動特性
★ 水文地質參數推估

關鍵字(英)

★ Big data mining
★ Extended empirical orthogonal function
★ Groundwater flow
★ Hydrogeological parameters estimation

論文目次

摘要????????i
Abstract????????ii
誌謝????????iii
目錄????????iv
圖目錄????????vi
表目錄????????viii
第一章緒論????????1
1-1 研究背景與動機 ????????1
1-2 研究問題與目的????????3
1-3 研究流程????????4
1-4 論文結構????????5
第二章文獻回顧????????7
2-1 地下水資源管理之挑戰????????7
2-2 屏東平原地下水之相關研究????????9
2-3 資料探勘應用於地下水資源管理????????11
2-3-1 經驗正交函數????????11
2-3-2 類神經網路 ????????12
2-3-3 廣義可增式模型 ????????13
2-4 水文地質參數推估????????14
第三章研究方法????????16
3-1 研究架構????????16
3-2 研究區域概述????????17
3-2-1 區域地理環境概述????????18
3-2-2 水文地質架構概述????????19
3-3 資料蒐集及描述????????21
3-3-1 地下水位觀測資料????????21
3-3-2 蓄水係數、滲透係數及粒徑資料????????23
3-4 特徵萃取與特徵重要性評估方法????????28
3-4-1 經驗正交函數(EOF)????????28
3-4-2 擴展經驗正交函數分解????????30
3-4-3 地理空間特徵萃取????????32
3-4-4 隨機森林特徵重要性評估????????35
3-5 廣義可增式模型????????38
3-6 類神經網路????????39
3-6-1 單層感知機器????????39
3-6-2 多層神經網路????????41
3-6-3 倒傳遞類神經網路????????42
3-7 水文地質參數推估模式????????44
3-8 交叉驗證????????46
第四章結果分析與討論????????48
4-1 擴展經驗正交函數分解結果????????48
4-2 特徵重要性分析結果????????55
4-3 廣義可增式模型結果分析????????59
4-4 類神經網路推估結果????????61
第五章結論與建議????????67
5-1 結論????????67
5-2 建議????????68
5-3 貢獻????????69
參考文獻????????70
評審意見回覆表????????76

參考文獻

1. 張良正、蔡威平、陳宇文(1999b)，「屏東地區地下水補注量推估及分級」，第三屆地下水資源及水質保護研討會，第65-76頁。
2. 徐鐵良(1961)，「台灣南部屏東谷地之自升地下水系」，中國地質學會會刊第4號(臺北市: 中國地質學會, 第73-81頁。
3. 徐年盛、江崇榮、汪中和等(2011)，「地下水系統水平衡分析與補注源水量推估之研究」，中國土木水利工程學刊，第二十三卷，第四期，第347-257頁。
4. 柯亭芳、丁澈士、吳峰誼(1996)，「屏東平原地下水變動立體化模擬及補助量估算之研究」，中國土木水利工程學刊，第二十二卷，第四期，第35-45頁。
5. 吳銘志 (1997)，「屏東沖平原內自流井之分佈及其賦存層之地層組構.第二屆地下水資源及水質保護研討會論文集: 第947-954頁。
6. 水利署水文技術組(2016)，「屏東平原水文特徵分析與耦合地表地下水數值模擬應用」，水利署電子報，第0189期」。
7. Chen, Zhuoheng, et al. (2002), "Predicting average annual groundwater levels from climatic variables: an empirical model." Journal of Hydrology 260(1-4): 102-117.
8. Custodio, Emilio. (2002), "Aquifer overexploitation: what does it mean?" Hydrogeology Journal 10(2): 254-277.
9. Eckhardt, K and Ulbrich. (2003), "Potential impacts of climate change on groundwater recharge and streamflow in a central European low mountain range." Journal of Hydrology 284(1-4): 244-252.
10. Foster S.S.D., Chilton J., Moencg M., Cardy F. and Schiffler M. (2000), "Groundwater in rural development." World Bank Technical Paper NO. 463(World Bank, Washington D.C.): 101.
11. Foster, SSD and Chilton, PJ. (2003), "Groundwater: the processes and global significance of aquifer degradation." Philosophical Transactions of the Royal Society B: Biological Sciences 358(1440): 1957-1972.
12. Fried, Jean J. (1975),Groundwater pollution, Elsevier.
13. Gibson, John and Aggarwal, Pradeep. (2001), "REVISITING CLIMATE CHANGES." IAEA BULLETIN 43: 2.
14. IWMI. (2000), "Improving Water and Land Resources Management for Food." IWMI Strategic plan 2000-2005(Livelihoods and Nature): 30.
15. Moncaster, SJ, et al. (2000), "Migration and attenuation of agrochemical pollutants: insights from isotopic analysis of groundwater sulphate." Journal of Contaminant Hydrology 43(2): 147-163.
16. Pimentel, David, et al. (2004), "Water resources: agricultural and environmental issues." BioScience 54(10): 909-918.
17. Vorosmarty, Charles J, et al. (2000), "Global water resources: vulnerability from climate change and population growth." Science 289(5477): 284-288.
18. Wada, Yoshihide, et al. (2010), "Global depletion of groundwater resources." Geophysical research letters 37(20)
19. Aboufirassi, Mohamed and Marino, Miguel A. (1984), "Cokriging of aquifer transmissivities from field measurements of transmissivity and specific capacity." Journal of the International Association for Mathematical Geology 16(1): 19-35.
20. Ahmed, Shakeel and De Marsily, Ghislain. (1987), "Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity." Water Resources Research 23(9): 1717-1737.
21. Aziz, Abd, et al. (1992), "A neural?network approach to the determination of aquifer parameters." Groundwater 30(2): 164-166.
22. Breiman, Leo. (2001), "Random forests." Machine learning 45(1): 5-32.
23. Coppola, Emery A, et al. (2007), "Multiobjective analysis of a public wellfield using artificial neural networks." Groundwater 45(1): 53-61.
24. Coppola Jr, Emery, et al. (2003), "Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions." Journal of hydrologic Engineering 8(6): 348-360.
25. Cox, ME, et al. (2005), "Water quality condition and trend in North Queensland waterways." Marine Pollution Bulletin 51(1-4): 89-98.
26. Daliakopoulos, Ioannis N, et al. (2005), "Groundwater level forecasting using artificial neural networks." Journal of Hydrology 309(1-4): 229-240.
27. Dominici, Francesca, et al. (2002), "On the use of generalized additive models in time-series studies of air pollution and health." American Journal of Epidemiology 156(3): 193-203.
28. Egeberg, Morten. (2010), "The European Commission." European union politics 3: 125-140.
29. Friedman, Jerome H and Stuetzle, Werner. (1981), "Projection pursuit regression." Journal of the American statistical Association 76(376): 817-823.
30. Fukuoka, A. (1951), "The Central Meteorological Observatory, A study on 10-day forecast (A synthetic report)." Geophysical Magazine 22(3): 177-208.
31. Garcia, Luis A and Shigidi, Abdalla. (2006), "Using neural networks for parameter estimation in ground water." Journal of Hydrology 318(1-4): 215-231.
32. Giannitrapani, Marco, et al. (2005), "Additive models for correlated data with applications to air pollution monitoring." Biometrics
33. Govindaraju, Rao S. (2000), "Artificial neural networks in hydrology. I: Preliminary concepts." Journal of Hydrologic Engineering 5(2): 115-123.
34. Granger, Clive WJ. (1993), "Strategies for Modelling Nonlinear Time?Series Relationships." Economic Record 69(3): 233-238.
35. Hastie, Trevor J and Tibshirani, Robert J. (1990), Generalized additive models, volume 43 of Monographs on Statistics and Applied Probability, Chapman & Hall, London
36. Hastie, Trevor and Tibshirani, Robert. (1995), "Generalized additive models for medical research." Statistical methods in medical research 4(3): 187-196.
37. Hsieh, Shih Hsiung. (1972), "Subsurface Geology And Gravity Anomalies Of The Tainan And Chungchou Structures Of The Coastal Plain Of Southwestern Taiwan." Petroleum Geology of Taiwan 10: 323-338.
38. Hsu, Kuo-Chin, et al. (2007), "Climate-induced hydrological impacts on the groundwater system of the Pingtung Plain, Taiwan." Hydrogeology Journal 15(5): 903-913.
39. Hsu, Kuo?lin, et al. (1995), "Artificial neural network modeling of the rainfall?runoff process." Water Resources Research 31(10): 2517-2530.
40. Hu, Michael Jen-Chao. (1964), Application of the adaline system to weather forecasting, Department of Electrical Engineering, Stanford University
41. Jiongguang, Xie. (1995), "Extended Empirical Orthogonal Function (EEOF) and Applications to Monthly (Seasonal) Rainfall Prediction [J]." Scientia Atmospherica Sinica 4
42. Karamouz, Mohammad, et al. (2007), "Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources." Water International 32(1): 163-176.
43. Kholghi, M and Hosseini, SM. (2006), "Estimation of aquifer transmissivity using kriging, artificial neural network, and neuro-fuzzy models." Journal of Spatial Hydrology 6(2)
44. Kohavi, Ron. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, Montreal, Canada.
45. Letcher, RA, et al. (2001), "Methods for the analysis of trends in streamflow response due to changes in catchment condition." Environmetrics 12(7): 613-630.
46. Lorenz, Edward N. (1956), "Empirical orthogonal functions and statistical weather prediction."
47. Maier, Holger R and Dandy, Graeme C. (1996), "The use of artificial neural networks for the prediction of water quality parameters." Water Resources Research 32(4): 1013-1022.
48. Mascaro, Giuseppe, et al. (2015), "Hyperresolution hydrologic modeling in a regional watershed and its interpretation using empirical orthogonal functions." Advances in water resources 83: 190-206.
49. McCulloch, Warren S and Pitts, Walter. (1943), "A logical calculus of the ideas immanent in nervous activity." The bulletin of mathematical biophysics 5(4): 115-133.
50. McPhee, James and Yeh, William W-G. (2008), "Groundwater management using model reduction via empirical orthogonal functions." Journal of Water Resources Planning and Management 134(2): 161-170.
51. Motaghian, HR and Mohammadi, J. (2011), "Spatial estimation of saturated hydraulic conductivity from terrain attributes using regression, kriging, and artificial neural networks." Pedosphere 21(2): 170-177.
52. Mukhopadhyay, Amitabha. (1999), "Spatial estimation of transmissivity using artificial neural network." Groundwater 37(3): 458-464.
53. Nathan, RJ, et al. (1999). "On the application of generalised additive models to the detection of trends in hydrologic time series data". Water 99: Joint Congress; 25th Hydrology & Water Resources Symposium, 2nd International Conference on Water Resources & Environment Research; Handbook and Proceedings, Institution of Engineers, Australia.
54. Nayak, Purna C, et al. (2006), "Groundwater level forecasting in a shallow aquifer using artificial neural network approach." Water Resources Management 20(1): 77-90.
55. Ramsay, Timothy O, et al. (2003), "The effect of concurvity in generalized additive models linking mortality to ambient particulate matter." Epidemiology 14(1): 18-23.
56. Rogers, Leah L and Dowla, Farid U. (1994), "Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling." Water Resources Research 30(2): 457-481.
57. Rumelhart, David E, et al. (1994), "The basic ideas in neural networks." Communications of the ACM 37(3): 87-92.
58. Sahoo, S, et al. (2017), "Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US." Water Resources Research 53(5): 3878-3895.
59. Sharda, Ramesh. (1994), "Neural networks for the MS/OR analyst: An application bibliography." Interfaces 24(2): 116-130.
60. Shigidi, Abdalla and Garcia, Luis A. (2003), "Parameter estimation in groundwater hydrology using artificial neural networks." Journal of computing in civil engineering 17(4): 281-289.
61. Shiklomanov, Igor A and Rodda, John C. (2004), World water resources at the beginning of the twenty-first century, Cambridge University Press.
62. Sorichetta, Alessandro, et al. (2013), "A Comparison of Data?Driven Groundwater Vulnerability Assessment Methods." Groundwater 51(6): 866-879.
63. Swartzman, Gordon, et al. (1992), "Spatial analysis of Bering Sea groundfish survey data using generalized additive models." Canadian Journal of Fisheries and Aquatic Sciences 49(7): 1366-1378.
64. Vermeulen, PTM, et al. (2004), "Reduced models for linear groundwater flow models using empirical orthogonal functions." Advances in water resources 27(1): 57-69.
65. Weare, Bryan C and Nasstrom, John S. (1982), "Examples of extended empirical orthogonal function analyses." Monthly Weather Review 110(6): 481-485.
66. White, Halbert. (1989), "Learning in artificial neural networks: A statistical perspective." Neural computation 1(4): 425-464.
67. Yu, Hwa-Lung and Chu, Hone-Jay. (2010), "Understanding space–time patterns of groundwater system by empirical orthogonal functions: a case study in the Choshui River alluvial fan, Taiwan." Journal of Hydrology 381(3-4): 239-247.
68. Yu, Hwa-Lung and Lin, Yuan-Chien. (2015), "Analysis of space–time non-stationary patterns of rainfall–groundwater interactions by integrating empirical orthogonal function and cross wavelet transform methods." Journal of Hydrology 525: 585-597.
69. Zhang, Guoqiang, et al. (1998), "Forecasting with artificial neural networks:: The state of the art." International journal of forecasting 14(1): 35-62.

指導教授

林遠見(Yuan-Chien Lin)

審核日期

2018-7-25

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