請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44806完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 譚義績 | |
| dc.contributor.author | Hsien-Tsung Lin | en |
| dc.contributor.author | 林賢宗 | zh_TW |
| dc.date.accessioned | 2021-06-15T03:55:21Z | - |
| dc.date.available | 2015-06-30 | |
| dc.date.copyright | 2010-06-30 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-06-24 | |
| dc.identifier.citation | 1. 張斐章，張麗秋，類神經網路，台北：臺灣東華書局股份有限公司，2005。
2. 經濟部水利署，高屏大湖工程計畫試挖暨地下水文監測追蹤與檢討評估成果報告，2008。 3. 經濟部水利署，土庫農場淺層地下水開發可行性規劃，2010。 4. 翻宗吾，受薄壁效應影響的單井抽水試驗推估非受壓含水層水文參數之研究，2002，中央大學碩士論文。 5. Aziz ARA, Wong KFV. 1992. Neural network approach to the determination of aquifer parameters. Ground Water 30(2): 164-166. 6. Butler JJ, Liu WZ. 1991. Pumping tests in non-uniform aquifers- The linear strip case. J. Hydrol. 128: 69-99. 7. Carter JE. 1974. Solutions for laminar boundary layers with separation and reattachment, AIAA Paper 74-583, Palo Alto, Calif. 8. Chavent G. 1974. Identification of functional parameters in partial differential equations. In Identification of parameters in Distributed Systems, ed. R.E. Goodson & M. Polis. American Society of Mechanical Engineers, New York :31-48. 9. Chow VT. 1952. On the determination of transmissibility and storage coefficients from pumping test data, Trans. Amer. Geophysical Union 33:397-404. 10. Cooper HH, Jacob CE. 1946. A generalized graphical method for evaluating formation constants and summarizing well-field history, Trans. Amer. Geophysical Union 27: 526-534. 11. Coppola JR, Rana AJ, Poulton MM, Szidarovszky F, Uhl VW. 2005. A neural network model for predicting aquifer water level elevations. Ground Water 43(2): 231-241. 12. Dagan, G., 1989. Flow and transport in porous formations. Springer-Verlag, Berlin ; New York. 13. Daliakopoulos IN, Coulibaly P, Tsanis IK. 2005. Groundwater level forecasting using artificial neural networks, J. Hydrol. 309: 229-240. 14. Dawson K.J. and J.D. Istok, Aquifer Testing, Lewis Publishers,Michigan, 344pp, 1991. 15. Desbarats AJ. 1992. Spatial averaging of transmissivity in heterogeneous fields with flow toward a well. Water Resources Research 28(3):757–767 16. Domenico PA and Schwartz FW, 1990, Physical and chemical hydrogeology, John Wiley and Sons. 17. Eppstein MJ, Dougherty DE. 1996. Simultaneous estimation of transmissivity values and zonation, Water Resources Research 32(11):3321–3336. 18. Fletcher R. 1987. Practical Methods of Optimization. John Wiley & sons, New York. 19. Gutjahr AL. 1989. Fast Fourier transforms for random field generation. project report for Los Alamos grant, contract 4-R58-2690R, Dep. of Math., N. M. Tech., Socorro. 20. Gutjahr AL, Wilson JL. 1989. Co-kriging for stochastic flow models. Transport in Porous Media 4 (6):585–598. 21. Heilweil VM, Hsieh PA. Determining Anisotropic Transmissivity Using a Simplified Papadopulos Method. Ground Water 44:749-753. 22. Hoeksema RJ, Kitanidis PK. 1984. An application of the geostatistical approach to the inverse problem in two-dimensional groundwater modeling. Water Resources Research 20 (7): 1003–1020. 23. Indelman P, Fiori A, Dagan G. 1996. Steady flow toward wells in heterogeneous formations: Mean head and equivalent conductivity. Water Resources Research 32(7):1975-1983. 24. Indelman P. 2003. Transient pumping well flow in weakly heterogeneous formations. Water Resources Research 39(10):1287, doi:10.1029/2003WR002036 25. Jang CS, Liu CW, Chia Y, Cheng YH, Chen YC. 2008. Changes in hydrogeological properties in the Choushui River alluvial fan due to the 1999 Chi-Chi earthquake, Taiwan. Hydrogeology Journal 16: 389-397. 26. Jacob CE. 1944. Notes on determining permeability by pumping tests under water-table conditions. U.S. Geol. Surv. Open-file Rept. 27. Karahan H, Ayvaz MT. 2006. Forecasting aquifer parameters using artificial neural networks. Journal of Porous Media 9(5):429–444. 28. Karunanithi N, Grenney WJ, Whitley D, Bovee K, 1994, Neural networks for river flow prediction. Journal of Computing in Civil Engineering, ASCE 8(2): 201–220. 29. Lachassagne P, Ledoux E, Marsily GDE. 1989. Evaluation of hydrogeological parameters in heterogeneous porous medias. in Groundwater Management: Quantity and Quality, LAHS Publ. : 188,3-18. 30. Lin HT, Yu HL, Tan YC, Chen CH. 2008. Effective hydrogeological parameter estimation with a novel modified Tabu search algorithm. European Geophysical Union (EGU) General Assembly, Vienna, Austria, April 13-18 31. Lin GF, Chen GR. 2006. An improved neural network approach to the determination of aquifer parameters. Journal of Hydrology 316(1-4): 281–289. 32. Liu CW, Chou YL, Lin ST, Lin GJ, Jang CS. 2010. Management of High Groundwater Level Aquifer in the Taipei Basin, Water Resources Management (in press). 33. Matheron B. 1967. Elements Pour une Theorie des Milieus poreus, Maisson et Cie, Paris. :166. 34. McLaughlin D, Townley LR. 1996. A reassessment of the groundwater inverse problem. Water Resources Research 32(5):1131-1161. 35. Meier PM, Carrera J, Sanchez-Vila X. 1999. A numerical study on the relationship between transmissivity and specific capacity in heterogeneous aquifers. Ground Water 37(4): 611–617. 36. Meier PM, Carrera J, Sanchez-Vila X. 1998. An evaluation of Jacob's method for the interpretation of pumping tests in heterogeneous formations. Water Resources Research 34(5): 1011-1025. 37. Naff RL. 1991. Radial flow in heterogeneous porous media: an analysis of specific discharge. Water Resources Research 27(3):307–316. 38. Neuman SP, 1975, Analysis of pumping test data from anisotropic unconfined aquifers considering delayed gravity response Water Resources Research 11:329–342. 39. Oliver DS. 1993. The influence of non-uniform transmissivity and storativity on drawdown. Water Resources Research 29(1):169–178 40. Papadopulos I. 1965. Nonsteady flow to a well in an infinite anisotropic aquifer. in Proceedings of Dubrovnik Symposium on the Hydrology of Fractured Rocks, edited, International Association of Scientific Hydrology, Dubrovnik, Yugoslavia. :21-31 41. Painter SL, Woodbury AD, Jiang YF. 2007. Transmissivity estimation for highly heterogeneous aquifers: comparison of three methods applied to the Edwards Aquifer. Texas, USA, Hydrogeology Journal 15(2):315-331. 42. Prasad KL, Rastogi AK. 2001. Estimating net aquifer recharge and zonal hydraulic conductivity values for Mahi Right Bank Canal project area, India by genetic algorithm. Journal of Hydrology 243(3–4): 149–161. 43. Poeter EP, Hill MC. 1998. Documentation of UCODE, a computer code for universal inverse modelling. US Geological Survey, Water-Resources Investigations Report 98–4080. 44. Rizzo DM, and Dougherty DE. 1994. Characterization of aquifer properties using artificial neural networks: neural kriging, Water Resources Research 30(2): 483-497. 45. Rhode KL, Osiensky JL, Miller SM. 2007. Numerical evaluation of volumetric weighted mean transmissivity estimates in laterally heterogeneous aquifers. Journal of Hydrology 347:381-390. 46. Samani N, Gohari-Moghadam M, Safavi AA. 2008. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology 340(1-2): 1–11. 47. Sanchez-Vila X, Meier PM, Carrera J. 1999, Pumping test in heterogeneous aquifers: an analytical study of what can be obtained from their interpretation using Jacob’s method. Water Resources Research 35(4): 943-952 48. Skaggs TH, Kabala ZJ. 1998. Limitations in recovering the history of a groundwater contaminant plume. Journal of Contaminant Hydrology 33:347-359, 49. Streltsova TD. 1988. Well testing in heterogeneous formations. Wiley, New York Sudheer KP, Gosain AK, Rangan D, Saheb SM. 2002. Modelling evaporation using an artificial neural network algorithm. Hydrological Processes 16(16): 3189–3202. 50. Sun NZ, Yeh WWG. 1985. Identification of parameter structure in groundwater inverse problems. Water Resources Research 21: 869-83. 51. Tan CC, Tung CP, Chen CH, Yeh WWG. 2008. An integrated optimization algorithm for parameter structure identification in groundwater modeling. Advances in Water Resources 31(3): 545-560. 52. Tartakovsks DM, Guadagnini A, Guadagnini L. 2000. Effective hydraulic conductivity and transmissivity for heterogeneous aquifers. Mathematical Geology, 32(6):751-759. 53. Tartakovsky DM, Guadagnini A, Ballio F, Tartakovsky AM. 2002. Localization of mean flow and apparent transmissivity tensor for bounded randomly heterogeneous aquifers. Transport in Porous Media 49(1):41-58. 54. Theis CV. 1935. The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using ground-water storage. Trans. Amer. Geophysical Union 16: 519-524. 55. Ting CS, Kerh T, Liao CJ. 1998. Estimation of groundwater recharge using the chloride mass-balance method, Pingtung Plain, Taiwan. Hydrogeology Journal 6(2):282-292. 56. Thomas GB Jr. 1972. Calculus and analytical geometry, 3d edition. Addison-Wesley Publishing Company, Reading, Massachusetts. 57. Toth J. 1966. Groundwater, geology, movement, chemistry and resources, near Olds, Alberta. Research Council of Alberta Bulletin 17, Edmonton, 126. 58. Tumlinson LG, Osiensky JL, Fairley JP. 2006. Numerical evaluation of pumping well transmissivity estimates in laterally heterogeneous formations. Hydrogeology Journal 14 (1–2): 21–26. 59. Walton WC. 1962. Leaky artesian aquifer conditions in Illinois. Illinois State Water Survey, Illinois. 60. Warren JE, Price HS. 1961. Flow in heterogeneous media. Soc. Pet. Eng. J. 1:153-169 61. Weeks EP. 2004. Hydrologic properties of coal-beds in the Powder River Basin, Montana. II. Aquifer test analysis. Journal of Hydrology 308:242-257. 62. Wu SC, Tan YC, Chen CH, Lin ST, Ke KY. 2008. A two-dimensional inverse model to identify transmissivity in an anisotropic aquifer. Hydrological Processes 22:5086–5096 63. Wu CM, Yeh TC, Zhu J, Lee TH, Hsu NS, Chen CH, Sancho CH. 2005. Traditional analysis of aquifer tests: Comparing apples to oranges?, Water Resources Research 41, W09402, doi:10.1029/2004WR003717. 64. Vandenberg A. 1977a. Type curves for analysis of pump tests in leaky strip aquifers. J. Hydrol. 33:15-26. 65. Yeh WWG. 1986. Review of parameter-identification procedures in groundwater hydrology – the inverse problem. Water Resources Research 22(2):95–108. 66. Yu H L, Christakos G. 2006. Porous media upscaling in terms of mathematical epistemic cognition. Siam Journal on Applied Mathematics 66(2): 433-446. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44806 | - |
| dc.description.abstract | 含水層特性參數的調查是瞭解地下水流通特性的重要工作，含水層的空間變異性之大雖廣為認知，然目前利用抽水試驗的方式求得簡單的地下水有效水文參數，諸如有效流通係數、有效水力傳導係數、儲水係數等等。本研究利用修正型ANN模式、修正型PCA-ANN模式結合Papadopoulos解析解運用於地下水文有效參數推估以提高精確性，其所推估有效參數近似於幾何平均值。另外，再利用曲線法、直線法所推估參數代入Papdopulous 解析解運用於濁水溪沖積扇之現地抽水試驗(溪洲站)。由結果顯示，修正型ANN模式及修正型PCA-ANN模式所推求目標函數誤差值較小，原因為類神經網路模擬抽水試驗資料與井函數建立關係以優選最佳參數值，並建立一套推定含水層參數(儲水係數、流通係數)之自動程序，提高推求參數之精確性；而曲線法與直線法之目標函數較大原因，圖形套配過程中容易造成人為誤差以致目標函數提高。當水文地質參數推估確定後，本研究在區域尺度中，利用MODFLOW-96模式模擬地下水流空間分佈初步優選研究區域最佳地下水可開發水量區域，並希爾法以安全出水量驗證。優選後之最佳地下水可開發水量區域，並進行現地抽水試驗，依據洩降水位資訊利用修正型ANN模式推求滲透性方向性及大小，並利用滲透性較佳水文地質之方向性進一步優選抽水井位置最佳配置，將可提高抽水率，以提供地下水管理之參考。 | zh_TW |
| dc.description.abstract | Obtaining reasonable hydrological parameters is a key challenge in groundwater modeling. The analysis of the temporal evolution of pump-induced drawdown is a common approach to estimate the effective transmissivity and storage coefficients in the heterogeneous aquifer. In this study, we compared modified ANN model, modified PCA-ANN model cooperated with Papadopoulos solution, and applied them to the estimation of effective aquifer parameters with better precision. Results show that modified ANN model and modified PCA-ANN model can efficiently obtain the most representative (or effective) aquifer parameters wich are close to geometric means. In-situ time-drawdown from Shi-Chou station on the Choushui River alluvial fan, Taiwan, is further adopted to test the applicability and reliability of the proposed methods, as well as provide a basis for comparison with the Straight-line method (SLM) and the Type-curve method (TCM). Results show that both of the modified methods give better estimation than SLM and TCM in terms of RRMSE. Besides, model uncertainties could be easily induced with the application of the TCM or SLM for not using a systematic approach to optimize the parameters of well functions. In order to diminish the uncertainties, relationships between well function and simulated drawdown are established by modified ANN and modified PCA-ANN to automatically optimize those parameters. Aforementioned methods are further applied to optimize the location of pumping wells in a local-scaled numerical model, MODFLOW-96. The optimal zone for exploiting groundwater could be initially decided by modeling the groundwater flow distribution, and validated by Hill method. Pumping tests are carried out in the optimal zone to generate time-drawdown data for modified ANN method to estimate the value and direction of transmissivities. Optimization of the location of the pumping wells is conducted in the direction with major transmissivities in order to increase pumping efficiency. This study expects to develop a mechanism of parameters optimization for groundwater management. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T03:55:21Z (GMT). No. of bitstreams: 1 ntu-99-D95622003-1.pdf: 4480476 bytes, checksum: 78ce103dc912a6af8613a204c610c070 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 目 錄
口試委員會審定書 謝誌 I 摘要 II Abstract III 目錄 V 圖目錄 VII 表目錄 XI 第一章 緒論 1 1.1 研究動機 1 1.2研究目的 2 1.3研究流程與架構概述 3 1.4 論文架構 5 第二章 文獻回顧 6 2.1 有效地下水文參數推估 6 2.2 地下水水流模式參數辨識之相關研究 9 第三章 地下水基本理論及相關方法之概述 13 3.1 地下水流方程式 13 3.2 Papadopoulos解析解(完全貫穿井解析解) 19 3.3倒傳遞類神經網路(Back Propagation Network) 22 3.4主成份分析 28 3.5倒傳遞類神經與Papadopoulos解析解結合 30 3.6 含水層序率特性與異質性 35 第四章 非均勻網格模式建置 37 4.1 數值模式離散化 37 4.2 數值模式驗證 43 第五章 地下水含水層特性探討 45 5.1產生均質水文參數-抽水試驗 45 5.2產生異質性水文參數-抽水試驗 53 5.3現地抽水試驗-濁水溪沖積扇 63 第六章 抽水設施位置最佳配置 67 6.1 研究區域概述 69 6.1.1區域位置 69 6.1.2 氣象與雨量 70 6.1.3 水文地質參數及含水層特性 71 6.2 研究區域數值模式概念化 74 6.2.1網格劃分及邊界條件 74 6.2.2研究區域-數值模式參數設定 75 6.3地下水數值模式校正與驗證 77 6.4 土庫農場現地抽水試驗及分析 86 第七章 結論與建議 96 7.1結論 96 7.2建議 97 參考文獻 98 附錄A: Papdopulous解析解 105 附錄B: Jacob 修正非拘限含水層洩降水位 112 附錄C : Neuman標準曲線套圖 114 圖 目 錄 圖1-1 研究流程示意圖 4 圖2-1 (a)產生現地異質場之流通係數空間分佈(b)現地抽水試驗之不同 時間洩降水位空間分佈圖 9 圖3-1 微小元素控制體積進出量示意圖 15 圖3-2 Theis曲線法觀測資料與井函數對圖關係示意圖 20 圖3-3 Cooper-Jacob直線法與觀測資料對圖關係推估參數示意圖 21 圖3-4 類神經數學模型 22 圖3-5 倒傳遞類神經網路 23 圖3-6 活化函數(a)門檻值函數(b)片段線性函數(c)S型函數(d)雙曲線函數 24 圖3-7 倒傳遞類神經網路演算流程圖 27 圖3-8 利用類神經網路對圖方式(前半段第一點資料對圖及段半段 穩定水位)影響推求參數示意圖 32 圖3-9 修正Lin and Chen類神經網路資料選 33 圖3-10 修正型Lin and Chen類神經網路圖(a)訓練過程(b)驗證過程 33 圖3-11 修正型PCA-ANN類神經網路圖(a)訓練過程(b)驗證過程 34 圖4-1 (左圖)均勻網格建置模式無法精確描述邊界情況，導致求解 之精確性降低。(右圖)非均勻網格建置模式，以致求解精確性提高 37 圖4-2 有限控制體示意圖 38 圖4-3 隱性法求解示意圖 40 圖4-4 地下水模式主矩陣之九帶寬矩陣示意圖 42 圖4-5 抽水量固定，抽水井與觀測井不同距離情況下，解析解與數值模式 模擬洩降水位比較圖 44 圖4-6 抽水井與觀測井距離固定、抽水量不同情況下，解析解與數值模式 模擬洩降水位比較圖 44 圖5-1類神經網路訓練後，修正型ANN模式及修正型PCA-ANN模式推 求RMSE值(中位數表示) 48 圖5-2 抽水試驗之洩降曲線定義前段時間及後段時間示意圖 50 圖5-3不同隱藏層個數及在觀測時間紀錄(觀測井時間總共為20)優選之 RRMSE (a)修正型ANN模式 (b)修正型PCA-ANN模式 50 圖5-4修正型ANN模式推求1000組地下水文參數與假設值之比較 51 圖5-5 修正型PCA-ANN模式推求1000組地下水文參數與假設值之比較 52 圖5-6 (左圖)二維流通係數Txx異質場空間分佈示意圖(右圖)抽水井與三 口觀測井相對位置關係圖 54 圖5-7不同隱藏層個數及在觀測時間紀錄(觀測井時間總共為20)優選之 RRMSE (a)修正型ANN模式 (b)修正型PCA-ANN模式 58 圖5-8利用前段時間及後段時間推求RMSE以機率分佈函數及累積分佈 函數表示圖(a)修正型ANN模式(B)修正型PCA-ANN模式 59 圖 5-9 修正型ANN模式及修正型PCA-ANN模式與原先方法所推求 RMSE比較圖 60 圖5-10a修正型ANN (19,14,16)及修正型PCA-ANN (1,14,16)推求流通 係數Txx之機率分佈函數圖 61 圖5-10b修正型ANN (19,14,16)及修正型PCA-ANN (1,14,16)推求流通 係數Tyy之機率分佈函數圖 61 圖5-10c修正型ANN (19,14,16)及修正型PCA-ANN (1,14,16)推求流通 係數Txy之機率分佈函數圖 62 圖5-10d修正型ANN (19,14,16)及修正型PCA-ANN (1,14,16)推求儲水 係數S之機率分佈函數圖 62 圖5-11 濁水溪沖積扇於溪洲站進行抽水試驗示意圖 64 圖5-12 四種方法推求模擬洩降水位與觀測水位比較圖 66 圖6-1 高屏地區生活及工業用水供需比較圖 67 圖6-2 高屏溪流域衛星影像及研究區域圖 69 圖6-3a 里港地區地質剖面位置圖 71 圖6-3b 里港地區地質剖面圖(A-A剖面) 72 圖6-4a 高屏大湖研究區域水文地質觀測站分佈圖 72 圖6-4b 高屏大湖研究區域地質剖面圖 73 圖6-5 建置研究區域模擬範圍網格及邊界示意圖 74 圖6-6 模擬範圍觀測井之地下水位資料 76 圖6-7 核發水權量之抽水井位置建置模式示意圖 76 圖6-8a 模擬範圍分區以UCODE模式進行參數推估 79 圖6-8b 高屏大湖建置區域圖 79 圖6-9 經模式率定後之各觀測井觀測值及模擬值比較圖 80 圖6-10 經模式率定後之各觀測井觀測值及模擬值比較圖 81 圖6-11a 數值模式無建置高屏大湖模擬後地下水空間分佈圖(A)枯水季(B)豐水季 82 圖6-11b 數值模式建置高屏大湖模擬後地下水空間分佈圖(A)枯水季(B)豐水季 83 圖6-12 三組抽水井群位置示意圖 84 圖6-13 希爾法之抽水量與地下水水位變化量之關係圖 85 圖6-14 抽水試驗井及觀測井相對位置示意圖 86 圖6-15 抽水試驗後觀測井及抽水井之洩降資料圖 89 圖6-16 未抽水試驗之各觀測井剖面圖 89 圖6-17 抽水試驗六口觀測井洩降水位圖 92 圖6-18 觀測井2號之標準曲線疊合 93 圖6-19 現地抽水試驗推求有效流通係數方向性之極座標圖 94 圖6-20a 滲透性係數較佳區域佈置抽水井示意圖 94 圖6-20b 滲透性係數較佳區域佈置集水廊道示意圖 95 圖6-21 土庫農場之抽水試驗至洩降穩定之洩降水位圖 95 圖A 非拘限含水層抽水試驗之洩降水位剖面圖 113 圖B 觀測資料前段時間與套配曲線密合示意圖 114 表 目 錄 表4-1各觀測井與抽水井距離表 43 表4-2 不同模擬情境之抽水量變化表 43 表5-1 類神經網路訓練過程使用之參數 46 表5-2 修正型ANN (19,10,10)及修正型PCA-ANN (1,10,10)最佳優選 之RRMSE 及 RMSE 52 表5-3 地下水含水層之水文地質參數表 54 表5-4 最佳修正型ANN (19,14,16)及PCA-ANN (1,14,16)推求300組 隨機場址之地下水文參數 60 表5-5 抽水試驗對三口觀測井影響洩降水位表 65 表5-6利用四種方法進行推求各參數之中位數表 66 表6-1 計畫區域的水力傳導係數 73 表6-2 利用希爾法(Hill method)推求三組抽水井群之安全出水量 (時間為2008/10至2009/10) 85 表6-3 新建抽水井及觀測井建構詳細資料表 87 表6-4 各觀測井率定前之洩降水位表 90 表6-5 各觀測井率定後之洩降水位表 91 表6-6 現地抽水試驗推求各種觀測井組合之水文參數 92 表6-7 現地抽水試驗推求各種觀測井組合之主軸向流通係數及方向性表 93 | |
| dc.language.iso | zh-TW | |
| dc.subject | 有效水文參數 | zh_TW |
| dc.subject | 修正型ANN模式 | zh_TW |
| dc.subject | 修正型PCA-ANN模式 | zh_TW |
| dc.subject | 抽水試驗 | zh_TW |
| dc.subject | 希爾法 | zh_TW |
| dc.subject | modified ANN model | en |
| dc.subject | effective hydrological parameters | en |
| dc.subject | Hill method | en |
| dc.subject | pumping test | en |
| dc.subject | modified PCA-ANN | en |
| dc.title | 利用類神經網路推估非等向性含水層參數之研究 | zh_TW |
| dc.title | Estimating Anisotropic Aquifer Parameter by Artificial Neural Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳主惠,李振誥,劉振宇,許少華,余化龍 | |
| dc.subject.keyword | 修正型ANN模式,修正型PCA-ANN模式,抽水試驗,希爾法,有效水文參數, | zh_TW |
| dc.subject.keyword | modified ANN model,modified PCA-ANN,pumping test,Hill method,effective hydrological parameters, | en |
| dc.relation.page | 114 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-06-25 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物環境系統工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-99-1.pdf 未授權公開取用 | 4.38 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。