請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6657
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張斐章(Fi-John Chang) | |
dc.contributor.author | Cheng-Hua Lin | en |
dc.contributor.author | 林政華 | zh_TW |
dc.date.accessioned | 2021-05-17T09:15:52Z | - |
dc.date.available | 2013-08-20 | |
dc.date.available | 2021-05-17T09:15:52Z | - |
dc.date.copyright | 2012-08-20 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-07 | |
dc.identifier.citation | 1. Anawar, H.M., Akai, J., Komaki, K., Terao, H., Yoshioka, T., Ishizuka, T., Safiullah, S. and Kato, K. (2003) Geochemical occurrence of arsenic in groundwater of Bangladesh: sources and mobilization processes. Journal of Geochemical Exploration 77(2-3), 109-131.
2. Anawar, H.M., Akai, J. and Sakugawa, H. (2004) Mobilization of arsenic from subsurface sediments by effect of bicarbonate ions in groundwater. Chemosphere 54(6), 753-762. 3. Berg, M., Tran, H.C., Nguyen, T.C., Pham, H.V., Schertenleib, R. and Giger, W. (2001) Arsenic contamination of groundwater and drinking water in Vietnam: A human health threat. Environmental Science & Technology 35(13), 2621-2626. 4. Chang, F.J., Chang, L.C. and Huang, H.L. (2002) Real-time recurrent learning neural network for stream-flow forecasting. Hydrological Processes 16(13), 2577-2588. 5. Chang, F.J., Chang, L.C., Kao, H.S. and Wu, G.R. (2010) Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. Journal of Hydrology 384(1-2), 118-129. 6. Chang, F.J. and Chang, Y.T. (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources 29(1), 1-10. 7. Chang, F.J. and Chen, Y.C. (2001) A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of Hydrology 245(1-4), 153-164. 8. Chang, F.J. and Chen, Y.C. (2003) Estuary water-stage forecasting by using radial basis function neural network. Journal of Hydrology 270(1-2), 158-166. 9. Chang, F.J., Hu, H.F. and Chen, Y.C. (2001) Counterpropagation fuzzy-neural network for streamflow reconstruction. Hydrological Processes 15(2), 219-232. 10. Chang, F.J., Kao, L.S., Kuo, Y.M. and Liu, C.W. (2010) Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in Taiwan. Journal of Hydrology 388(1-2), 65-76. 11. Chang, L.C. and Chang, F.J. (2001) Intelligent control for modelling of real-time reservoir operation. Hydrological Processes 15(9), 1621-1634. 12. Chang, Y.T., Chang, L.C. and Chang, F.J. (2005) Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves. Hydrological Processes 19(7), 1431-1444. 13. D.S. Broomhead and D. Lowe (1998) Multivariate Function Interpolation and Adaptive Networks. Complex Systems 2: 321-355. 14. Goh, F., Lim, C., Sih, V.K.T., Ismail, Z. and Chooi, S.Y.M. (2005) Occurrence of arsenic-based defects and techniques for their elimination. Ultra Clean Processing of Silicon Surfaces Vii 103-104, 87-90. 15. Grieu, S., Thiery, F., Traore, A., Nguyen, T.P., Barreau, M. and Polit, M. (2006) KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process. Chemical Engineering Journal 116(1), 1-11. 16. Harvey, C.F., Swartz, C.H., Badruzzaman, A.B.M., Keon-Blute, N., Yu, W., Ali, M.A., Jay, J., Beckie, R., Niedan, V., Brabander, D., Oates, P.M., Ashfaque, K.N., Islam, S., Hemond, H.F. and Ahmed, M.F. (2002) Arsenic mobility and groundwater extraction in Bangladesh. Science 298(5598), 1602-1606. 17. Kim, M.J., Nriagu, J. and Haack, S. (2000) Carbonate ions and arsenic dissolution by groundwater. Environmental Science & Technology 34(15), 3094-3100. 18. Kim, M.J., Nriagu, J. and Haack, S. (2002) Arsenic species and chemistry in groundwater of southeast Michigan. Environmental Pollution 120(2), 379-390. 19. Kohonen, T. (1982) Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43(1), 59-69. 20. Kohonen, T. (1995) Self-Organizing Maps. Berlin: Springer-Verlag. 21. Kuo, Y.M. and Chang, F.J. (2010) Dynamic Factor Analysis for Estimating Ground Water Arsenic Trends. Journal of Environmental Quality 39(1), 176-184. 22. Lee, B.H. and Scholz, M. (2006) Application of the self-organizing map (SOM) to assess the heavy metal removal performance in experimental constructed wetlands. Water Research 40(18), 3367-3374. 23. Lee, J.J., Jang, C.S., Wang, S.W., Liang, C.P. and Liu, C.W. (2008) Delineation of spatial redox zones using discriminant analysis and geochemical modelling in arsenic-affected alluvial aquifers. Hydrological Processes 22(16), 3029-3041. 24. Liu, C.W., Lin, K.H. and Kuo, Y.M. (2003) Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Science of the Total Environment 313(1-3), 77-89. 25. Lu, K.L., Liu, C.W., Wang, S.W., Jang, C.S., Lin, K.H., Liao, V.H.C., Liao, C.M. and Chang, F.J. (2010) Primary sink and source of geogenic arsenic in sedimentary aquifers in the southern Choushui River alluvial fan, Taiwan. Applied Geochemistry 25(5), 684-695. 26. McArthur, J.M., Ravenscroft, P., Safiulla, S. and Thirlwall, M.F. (2001) Arsenic in groundwater: Testing pollution mechanisms for sedimentary aquifers in Bangladesh. Water Resources Research 37(1), 109-117. 27. McCreadie, H. and Blowes, D.W. (2000) Influence of reduction reactions and solid phase composition on porewater concentrations of arsenic. Environmental Science & Technology 34(15), 3159-3166. 28. NRC (2001) Arsenic in Drinking Water. Washiongton, DC: National Academy Press. 29. Park, J.M., Lee, J.S., Lee, J.U., Chon, H.T. and Jung, M.C. (2006) Microbial effects on geochemical behavior of arsenic in As-contaminated sediments. Journal of Geochemical Exploration 88(1-3), 134-138. 30. Pierce, M.L. and Moore, C.B. (1982) Adsorption of Arsenite and Arsenate on Amorphous Iron Hydroxide. Water Research 16(7), 1247-1253. 31. Polya, D.A., Gault, A.G., Diebe, N., Feldman, P., Rosenboom, J.W., Gilligan, E., Fredericks, D., Milton, A.H., Sampson, M., Rowland, H.A.L., Lythgoe, P.R., Jones, J.C., Middleton, C. and Cooke, D.A. (2005) Arsenic hazard in shallow Cambodian groundwaters. Mineralogical Magazine 69(5), 807-823. 32. Powell M. J. D. (1987) Radial Basis Function for Multivariable Interpolation: A Review. IMA Conference on Algorithms for Approximation of Function and Data. RMCS, Shrivenham, England. 143-167. 33. Raessler, M., Michalke, B., Schulte-Hostede, S. and Kettrup, A. (2000) Long-term monitoring of arsenic and selenium species in contaminated groundwaters by HPLC and HG-AAS. Science of the Total Environment 258(3), 171-181. 34. Sanchez-Martos, F., Aguilera, P.A., Garrido-Frenich, A., Torres, J.A. and Pulido-Bosch, A. (2002) Assessment of groundwater quality by means of self-organizing maps: Application in a semiarid area. Environmental Management 30(5), 716-726. 35. Schreiber, M.E., Simo, J.A. and Freiberg, P.G. (2000) Stratigraphic and geochemical controls on naturally occurring arsenic in groundwater, eastern Wisconsin, USA. Hydrogeology Journal 8(2), 161-176. 36. Shimada, N. (1996) Geochemical conditions enhancing the solubilization of arsenic into groundwater in Japan. Applied Organometallic Chemistry 10(9), 667-674. 37. Smedley, P.L. and Kinniburgh, D.G. (2002) A review of the source, behaviour and distribution of arsenic in natural waters. Applied Geochemistry 17(5), 517-568. 38. Tseng, W. P., Chen, W. Y.,Sung, J. L. and Chen J. S. (1961) A Clinical Study of Blackfoot Disease in Taiwan, An Endemic Peripheral Vascular Disease. Memoire College Med., National Taiwan University, 7, 1-18. 39. USEPA (2006) Drinking Water Standards. http://www.epa.gov/ 40. WHO (2006) Guidelines for Drinking-Water Quality. http://www.who.int/ 41. Xu, H., Allard, B. and Grimvall, A. (1988) Influence of Ph and Organic-Substance on the Adsorption of as(V) on Geologic Materials. Water Air and Soil Pollution 40(3-4), 293-305. 42. 水利署中區水資源局,2010,「湖山水庫工程計畫」,http://www3.wracb.gov.tw/index.asp。 43. 李品輝,2009,「以類神經網路探討全台蒸發量區域性分類與推估之成效」,國立臺灣大學生物環境系統工程學研究所碩士論文。 44. 高力山,2011,「人工智慧應用於區域地下水系統中砷污染推估之研究」,國立臺灣大學生物環境系統工程學研究所博士論文。 45. 高慧珊,2007,「以自組特徵映射網路推估蒸發量」,國立臺灣大學生物環境系統工程學研究所碩士論文。 46. 國立成功大學台南水工試驗所,1997~1998,「雲林離島式基礎工業區整體開發規劃調查研究報告」,第一部份,第七冊。 47. 張斐章、張麗秋,2010,「類神經網路導論 – 原理與應用」,滄海書局。 48. 陳文福、呂學諭、劉聰桂,2010,「台灣地下水之氧化還原狀態與砷濃度」,農業工程學報,第56卷第2期。 49. 曾文賓,1976,「烏腳病之診斷、治療與預防」,臺灣省政府烏腳病防治中心,第一輯,1-25。 50. 維基百科網站,www.wikipedia.org/。 51. 劉振宇、王聖瑋、盧光亮,2006,「台灣地區濁水溪沖積扇南翼之地下水砷污染可能來源與成因」,台灣土壤及地下水環境保護協會簡訊,第二十一期,第15頁-第19頁。 52. 歐東坤,2005,「嘉南地區地下水砷濃度之研究」,國立臺灣大學生物環境系統工程學研究所碩士論文。 53. 蔡文柄,2007,「應用類神經網路推估溪流之生物多樣性」,國立臺灣大學生物環境系統工程學研究所碩士論文。 54. 環保署,1998,「飲用水水源水質標準」,http://www.epa.gov.tw/。 55. 環保署,2000,「飲用水水質標準」,http://www.epa.gov.ew/。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6657 | - |
dc.description.abstract | 臺灣西南部沿海地區過去為烏腳病流行地區,經研究發現此情況與當地居民長期飲用含砷量高之地下水有密切關係,雖然當地居民已不再直接飲用地下水,但仍大量抽取地下水供給灌溉、養殖、公共及民生用水等多項用水標的,此舉可能造成砷透過生物累積過程進而對人體健康造成危害,實有必要建立可靠之推估模式,掌握該區域地下水砷污染情形,並分析該區域地下水之水質特性。
本研究目的為探討臺灣西南部沿海地區的水質特徵、影響地下水砷濃度消長之因子與可能影響砷釋出於地下水中的機制,並推估此區域地下水砷濃度之空間分布。本研究採用水利署於雲林縣沿海地區設置28座監測井之水質資料作為分析對象,首先將所有監測井水質資料輸入類神經網路-自組特徵映射網路(Self-Organizing Feature Map, SOM)進行聚類分析,經由視覺化結果呈現有效率地檢視水質因子間暨水質因子與砷濃度之關係,並由聚類的結果對測站進行分類以探討砷濃度在空間上的分布關係。進一步將自組特徵映射網路(SOM)的聚類結果移除砷的資訊作為決定輻狀基底函數類神經網路(Radial Basis Function Neural Network, RBFNN)之隱藏層神經元數目及中心點,再以RBFNN計算隱藏層至輸出層間神經元之權重值,以有限之水質因子推估砷濃度。上述推估結果亦與倒傳遞類神經網路(BPNN)模式的推估結果作比較,結論是SOM與RBFNN建立之模式精確度優於BPNN,且SOM於聚類時加入砷濃度資訊可使分類的結果更為顯著,並增加推估精確度;聚類分析的結果指出沿海地區測站地下水層鹽化與高砷污染情況,導致部分測站存在著高濃度砷,推論可能原因為此區域地下水環境趨於還原環境(如環境中存在高pH值、高鹼度、低溶氧量與低硫酸鹽濃度等),有利於砷釋出,此結論與國內外相關研究結果一致。最後,應用地理資訊系統(GIS)將本模式推估之地下水砷濃度繪製地下水砷污染區域潛勢圖,呈現研究區域內砷濃度推估結果在1998年與1999年在空間上的變化,經由輸入水質因子便可了解此區域地下水中砷之釋出與遷移情形。 本模式特色在於可藉由SOM拓樸圖建構與解釋砷濃度與水質因子間關係,同時聚類結果有助於RBFNN網路之砷濃度推估能力,並以GIS呈現區域地下水砷濃度空間分布情形,展現以水質因子推估區域砷濃度分布圖,可供決策者透過視覺化了解區域時間與空間上的砷污染情形。 | zh_TW |
dc.description.abstract | In the past, Blackfoot disease commonly occurred along the southwestern coast of Taiwan. A number of investigations revealed this epidemic disease was highly related to arsenic (As) concentration in groundwater, which is the main source of drinking water to local residents. Although local residents do not directly drink groundwater any more in the Yun-Lin County of Taiwan, groundwater is still a main water source in this area because surface water suffers from limited sources. A large quantity of groundwater has been extracted from the aquifer for supplying water to the public, fish ponds and crop lands, which has resulted in the accumulation of arsenic in crops and fish. Products highly-contaminated with arsenic has threatened the health of residents. Therefore, it’s essential to construct a reliable model for estimating As concentration in groundwater.
The aims of this study are to assess the characteristics of groundwater quality, extract the factors affecting As concentration, investigate the sources releasing As, and estimate As concentration in groundwater. The Water Resources Agency (WRA) have set up 28 monitoring wells for investigating groundwater pollution, and water quality data collected by the WRA were used in this study. The first subject of this study is to import all the data collected form 28 wells to the Self-Organizing Feature Map (SOM) network. The SOM was applied to classifying all the water quality data into a topology map for finding the hidden relations among data and the spatial patterns between water quality variables and As concentration. Then the clustering results were adopted as the centroids of the Radial Basis Function Neural Networks (RBFNN) for accurately estimating As concentration based on water quality variables. In addition, the Back Propagation Neural Network (BPNN) was built to compare with the proposed model that integrates the SOM and RBF. The results demonstrate that the performance of the proposed model is better than the BPNN. When comparing the clustering results, adding As concentration to the SOM could make the clustering results more obvious and therefore achieves much accurate estimation. Moreover, the results demonstrate the characteristics of groundwater quality in coastal areas correlate with salinization and arsenic pollution factors. According to the clustering results, we surmise that the occurrence of high arsenic concentration in parts of wells is mainly because groundwater is in the reduction phase, especially at higher pH and Alk values and lower dissolved oxygen levels and SO42- concentration. Finally, the Geographic Information System (GIS) is applied to the results of groundwater quality models for displaying the spatial distribution map of As pollution so that we can realize the temporal and spatial variation in arsenic concentration in the study area. | en |
dc.description.provenance | Made available in DSpace on 2021-05-17T09:15:52Z (GMT). No. of bitstreams: 1 ntu-101-R99622033-1.pdf: 2618598 bytes, checksum: d948a844515a82c697e183b1dfa27c63 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 誌 謝 i
摘要 iii ABSTRACT v 目錄 viii 圖目錄 x 表格目錄 xiii 第一章 緒論 1 1.1 研究緣起 1 1.2 研究目的 2 1.3 研究架構 4 第二章 文獻回顧 6 2.1 地下水中砷污染相關研究 6 2.2 分析地下水水質方法 9 2.3 自組特徵映射網路之應用 10 第三章 理論概述 12 3.1 類神經網路 12 3.1.1 自組特徵映射網路(SOM) 16 3.1.2 輻狀基底函數類神經網路(RBFNN) 22 3.1.3 倒傳遞類神經網路(BPNN) 27 3.2 交叉驗證法優選模式 33 3.3 評估指標 35 3.3.1 均方根誤差(RMSE) 35 3.3.2 平均絕對誤差(MAE) 36 3.3.3 正規均方誤差(NMSE) 36 3.3.4 相關係數(CC) 36 第四章 研究案例 37 4.1 研究區域概述 37 4.2 資料蒐集與處理 37 4.2.1 資料蒐集 37 4.2.2 資料處理 45 4.3 模式架構 45 第五章 結果與討論 54 5.1 SOM拓樸架構 54 5.1.1 聚類內測站之位置分布 58 5.1.2 聚類內水質因子探討 60 5.2 區域地下水模式推估結果 68 5.2.1 輻狀基底函數類神經網路(RBFNN) 68 5.2.2 倒傳遞類神經網路(BPNN) 71 5.2.3 空間推估 71 第六章 結論與建議 79 6.1 結論 79 6.2 建議 81 第七章 參考文獻 82 | |
dc.language.iso | zh-TW | |
dc.title | 以類神經網路探討雲林沿海地區地下水砷濃度與水質特徵 | zh_TW |
dc.title | Artificial Neural Networks for Assessing Arsenic Concentrations and Characteristics of Groundwater Quality in the Coastal Area of Yun-Lin, Taiwan | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 劉振宇(Chen-Wuing Liu),張麗秋(Li-Chiu Chang),張良正(Liang-Cheng Chang),高力山(Li-Shan Kao) | |
dc.subject.keyword | 砷,地下水水質,類神經網路,自組特徵映射網路,輻狀基底函數類神經網路, | zh_TW |
dc.subject.keyword | Arsenic,Groundwater quality,Artificial neural network,Self-Organizing Feature Map,Radial Basis Function Neural Networks,As,ANN,SOM,RBFNN, | en |
dc.relation.page | 88 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2012-08-07 | |
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-101-1.pdf | 2.56 MB | Adobe PDF | 檢視/開啟 |
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。