進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-0407201915593400
論文名稱(中文) 結合缺水量預測與SDF曲線建構缺水應變方案
論文名稱(英文) Development of Drought Response Actions Based on Water Shortage Prediction and SDF Curves
校院名稱 成功大學
系所名稱(中) 水利及海洋工程學系
系所名稱(英) Department of Hydraulics & Ocean Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 陳仲廷
研究生(英文) Chung-Ting Chen
學號 N86064084
學位類別 碩士
語文別 中文
論文頁數 113頁
口試委員 指導教授-游保杉
口試委員-徐國錦
口試委員-蕭政宗
口試委員-陳憲宗
口試委員-陳昭銘
中文關鍵字 乾旱嚴重度-延時-頻率曲線  缺水量預測  類神經網路  支撐向量機  抗旱策略 
英文關鍵字 drought severity-duration-frequency curves  SDF  water shortage prediction  neural network  support vector machine  drought mitigation strategy 
學科別分類
中文摘要   本研究之宗旨為發展水庫乾旱之量化方法與提供未來乾旱之情勢評估,藉由結合「水庫缺水量預測模式」與「乾旱嚴重度-延時-頻率曲線」兩種方法,給予缺水應變方案之建議。
  首先,本研究模擬曾文-烏山頭水庫供水系統,並使用30至180日之公共用水缺水量建立水庫缺水量早期預測模式,以枯水期初期之蓄水量與待定尺度之過去標準化降雨指標作為輸入參數,選定線性迴歸、倒傳遞類神經網路與支撐向量機作為預測模式,其結果顯示,類神經網路與支撐向量機等機器學習模式在僅使用蓄水量作為輸入參數時就能有效預測曾文水庫未來之缺水量,並以每年11、12、1月時以類神經網路進行早期預測之預測能力更佳。
  本研究使用傳統統計之頻率分析發展乾旱嚴重度-延時-頻率(drought severity-duration-frequency curves, SDF)曲線,將水庫模擬「年最大公共用水缺水量」定義為乾旱嚴重度,並藉由K-S檢定發現一般極端值分布具有充分之統計代表性,得以計算不同重現期(頻率年)之合成公共用水缺水量,以繪製SDF曲線,並可將過去歷史乾旱事件進行對應,藉此量化過去事件發生之頻率。
  本研究參考近年相關抗旱策略計畫,研擬四種策略方案,將以上兩個模式進行整合。首先以SDF曲線之頻率分析結果,計算各頻率年應節約計畫需水量,並發展常態節水、聯合調度、公共用水節水、加強農業灌溉等不同程度之抗旱策略方案因應之;接著再以缺水量預測模式之結果,對應SDF曲線之頻率,藉此選擇相對應之抗旱策略方案,以因應未來不同程度之乾旱。本研究選擇2014年(民國103年)之嚴重乾旱事件作為分析案例,其結果顯示,該年枯水期初期90日內缺水為2~5年頻率、90至180日之缺水為5~10年頻率。
英文摘要 This study aims to quantify the properties of drought events and provide some suggestions for drought adaptions based on a drought prediction model. Firstly, the operation of Tsengwen-Wushantou Water Resources System was simulated to find potential water shortage events.
Secondly, the model “water shortage prediction of reservoir” with the inputs of “reservoir storage” or “standardized precipitation index (SPI)” was developed by applying the linear regression, backpropagation neural network (BPN) and support vector machine (SVM). Three performance indices (e.g. normalized root mean square error) were applied and revealed that BPN and SVM provide better prediction results than linear regression model.
Thirdly, the “drought severity-duration-frequency (SDF) curves” were constructed by applying the statistical frequency analysis methods. The “annual maximum water shortage for public use” was defined as drought severity. Then, Kolmogorov-Smirnov Test was used to identify the best-fit probability distribution (i.e. GEV) from several candidate probability distributions. The SDF curves can quantify the severity, duration and frequency of water shortage events. By mapping historical drought evens on SDF curves, the information about drought frequency can be derived.
Finally, this study provided four-level drought response actions based on SDF curves. Given a predicted water shortage, the predicted drought event can be mapped onto the SDF curves so that the properties of potential drought events can be identified and the suggestions for drought response actions can also be found.
論文目次 摘要 I
Extended Abstract II
致謝 VII
目錄 IX
表目錄 XI
圖目錄 XIII
第一章 緒論 1
 1-1 研究動機與目的 1
 1-2 文獻回顧 2
  1-2-1 乾旱嚴重度-延時-頻率曲線 2
  1-2-2 水庫缺水量早期預警 5
  1-2-3 機器學習模式 7
 1-3 論文組織架構 10
第二章 研究區域與資料概述 12
 2-1 研究區域介紹 12
 2-2 水文資料概述 14
 2-3 資料前處理 16
 2-4 歷史乾旱事件資料蒐集 19
第三章 缺水量預測模式 20
 3-1 預測模式架構 20
 3-2 模式理論 22
 3-3 模式參數設定 33
 3-4 評鑑指標與模式優選方法 36
 3-5 模式預測結果與比較 39
 3-6 小結 73
第四章 乾旱嚴重度-延時-頻率(SDF)曲線發展 74
 4-1 發展流程 74
 4-2 頻率分析方法 77
 4-3 機率分布之適合度檢定 82
 4-4 頻率分析結果 84
 4-5 乾旱歷史對應與設計乾旱 86
 4-6 小結 88
第五章 建構缺水應變方案 89
 5-1 水庫供水量缺口對策研擬 89
 5-2 早期預測與SDF之連結 92
  5-2-1 策略節水量計算 92
  5-2-2 結合SDF曲線與預測模式之應用 96
 5-3 小結 101
第六章 結論與建議 102
 6-1 結論 102
 6-2 建議 103
參考文獻 104
附錄一 標準化降雨指標建立 附1-1
附錄二 水庫系統模式 附2-1
附錄三 改善缺水量預測模式之變量比較 附3-1
參考文獻 1. 方世榮(1993),增訂版統計學導論,泰華文化事業有限公司,台北市,461-470。
2. 朱容練、朱吟晨、林士堯、劉俊志、陳永明(2015),「2014-2015年乾旱事件概述」,國家災害防救科技中心災害防救電子報,第124期。
3. 朱容練、黃柏誠、吳宜昭、陳淡容、林欣弘、林冠伶、于宜強(2018),「2018年臺灣乾旱事件分析」,國家災害防救科技中心研究報告。
4. 江衍銘(2002),「二階段動態回饋式類神經網路於流量預測」,國立臺灣大學生物環境系統工程學系暨研究所碩士論文。
5. 呂季蓉(2006),「臺灣南部地區長期乾旱趨勢分析之研究」,國立成功大學水利及海洋工程學系碩士論文。
6. 宋嘉文(2003),「氣候變遷對臺灣西半部地區降雨及乾旱影響之研究」,國立成功大學水利及海洋工程學系碩士論文。
7. 李明軒(2008),「支撐向量機與模糊推論於流量預報即時誤差修正之研究」,國立成功大學水利及海洋工程學系碩士論文。
8. 林志彥(2007),「臺灣乾旱特性變動與頻率分析之研究」,國立成功大學水利及海洋工程學系碩士論文。
9. 林焜詳(2016),「支撐向量機與隨機森林應用於颱風時雨量預報之比較」,國立成功大學水利及海洋工程學系碩士論文。
10. 前瞻基礎建設計畫水環境建設(2018),「台南山上淨水場供水系統改善工程計畫(核定本)」。
11. 前瞻基礎建設計畫水環境建設(2018,「曾文南化聯通管工程計畫(核定本)」。
12. 袁舴(2016),「時雨量系集預報之即時誤差修正」,國立成功大學水利及海洋工程學系碩士論文。
13. 張斐章、張麗秋、黃浩倫(2003),類神經網路理論與實務,臺灣東華書局股份有限公司,台北市,2-29。
14. 陳思尹(2016),「應用機器學習法於QPESUMS即時雨量預報」,國立成功大學水利及海洋工程學系碩士論文。
15. 陳昶憲、楊朝仲、王益文(1996),「類神經網路於烏溪流域洪流預報之應用」,中華水土保持學報, 27(4), 267-274。
16. 陳昶憲、鐘侑達、梁家瑋、王晉倫(2006),「颱風早期降雨預測」,中華水土保持學報,37(2),201-208。
17. 陳柏蒼、周乃昉(2014),「臺灣水資源乾旱預警系統建置之研究」,農業工程學報,第60卷第3期,頁1-29。
18. 陳順宇(1997),迴歸分析二版,泰華文化事業有限公司,台北市,2-1~2-6、5-5~5-7、5-48~5-49。
19. 陳潔(2012),「氣候變遷對曾文水庫缺水風險之衝擊」,國立成功大學水利及海洋工程學系碩士論文。
20. 陳儒賢(2003),「類神經網路於水文系統之研究」,國立臺灣大學土木工程學研究所博士論文。
21. 陳憲宗(2006),「支撐向量機及模糊推理模式應用於洪水水位之即時機率預報」,國立成功大學水利及海洋工程學系博士論文。
22. 童偉安、謝平城(2014),「以倒傳遞類神經網路建構颱風降雨預測模式」,中華水土保持學會103年年會論文,5-1。
23. 黃文政(2008),「水庫乾旱預警系統-風險型決策模式之發展與應用」,行政院國家科學委員會專題研究計畫。
24. 黃俊霖、蔡文炳、陳逸鴻、張斐章(2013),「應用類神經網路建立濁水溪水系長時距地下水位之預測模式」,102年度農業工程研討會。
25. 黃隆明、古緯中(2012),「應用倒傳遞類神經網路於PM10預測之研究」,水土保持學報,44(4),341-360。
26. 黃寶萱(2017),「應用分布水文‐土壤‐植被模式探討氣候變遷對水文量之影響」,國立成功大學水利及海洋工程學系碩士論文。
27. 經濟部(2011),「曾文南化烏山頭水庫治理及穩定南部地區供水計畫」。
28. 經濟部(2016),「節約用水常態化行動方案(核訂本)」。
29. 經濟部(2017),「臺灣南部區域水資源經理基本計畫(第1次檢討)(核定本)」。
30. 經濟部(2018),「產業穩定供水策略行動方案」。

31. 經濟部水利署,「水利災害應變學習中心-旱災事件」,2019年4月20日,取自http://llc.caece.net/category/event-type/drought-event/。
32. 經濟部水利署南區水資源局(2014),「水文環境變遷情境下嘉南地區水源調度運用方案研究」。
33. 經濟部水利署南區水資源局(2015),「氣候變遷下曾文、烏山頭水庫系統供水潛能分析檢討」。
34. 經濟部水利署南區水資源局(2015),「電廠至東口堰間輸水專管可行性規劃」。
35. 經濟部水利署南區水資源局(2017),「106年度曾文水庫淤積測量工作」。
36. 經濟部水利署南區水資源局(2018),「精進灌溉節水管理技術-以嘉南灌區為例(第二期)」。
37. 經濟部水利署南區水資源局(2018),「曾文溪及高屏溪水系之水資源風險管理計畫」。
38. 經濟部水利署水利規劃試驗所(2013),「曾文南化水庫聯通管輸水工程可行性分析」。
39. 經濟部水利署水利規劃試驗所(2018),「因應氣候變遷水源設施乾旱供水風險評估」。
40. 經濟部水資源局(2001),「水文設計應用手冊」。
41. 葉怡成(2003),類神經網路模式應用與實作,儒林圖書有限公司,台北市,4-5~ 4-41。
42. 臺灣嘉南農田水利會(2011),「烏山頭水庫第四次安全評估-淤積測量成果報告」。
43. 劉雅慈(2018),「石門水庫乾旱預警指標之研究」,國立成功大學水利及海洋工程學系碩士論文。
44. 劉鑌鋈(2009),「利用機器學習修正QPESUMS雷達估計降雨」,國立成功大學水利及海洋工程學系碩士論文。
45. Adarsh, S., Karthik, S., Shyma, M., Das, P.G., Parveen, A.T.S., Sruthi, N. (2018). Developing Short Term Drought Severity-Duration-Frequency Curves for Kerala Meteorological Subdivision, India Using Bivariate Copulas. Ksce Journal of Civil Engineering, 22(3), SI, 962-973.
46. Ali, Z., Hussain, I., Faisal, I., Nazir, H. M., Hussain, T., Shad, M. Y., Shoukry, A. M., Gani, S. H. (2017). Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model. Advances in Meteorology, vol. 2017, Article ID 5681308, 9 pages.
47. American Meteorological Society (1997). Meteorological drought – Policy statement, B. Am. Meteorol. Soc., 78, 847–849.
48. Atiquzzaman, M., & Kandasamy, J. (2016). Prediction of hydrological time-series using extreme learning machine. Journal of Hydroinformatics, 18(2), 345-353.
49. Bontempi, G., Taieb, S. B., & Borgne, Y.-A. L. (2013). Machine Learning Strategies for Time Series Forecasting. Machine Learning Group, Computer Science Department.
50. Chang, F., & Chang, Y. (2006). Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, 29(1), 1-10.
51. Chiang, Y. M., Chang, L. C., Chang, F. J. (2004) Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling. Journal of Hydrology, 290(3-4), 297-311.
52. Dalezios, N.R., Loukas, A., Vasiliades, L., Liakopoulos, E. (2000). Severity-duration-frequency analysis of droughts and wet periods in Greece. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 45(5), 751-769.
53. Deo, R. C., Kisi, O., & Singh, V. P. (2017). Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmospheric Research, 184, 149-175.
54. Dracup, J. A., Lee, K. S., Paulson, E. G. (1980). On the definition of droughts. Water Resources Research, 16(2), 297-302.
55. Dracup, J. A., Lee, K. S., Paulson, E. G. (1980). On the statistical characteristics of drought events. Water Resources Research, 16(2), 289-296.
56. Edwards, D. C. and T. B. McKee, “Characteristics of 20th century drought in the United States at multiple timescales”, Department of Atmospheric Science, Colorado State University: Fort Collins Climatology Report, No.97-2, 1997.
57. Ganguli, P., & Reddy, M. J. (2014). Ensemble prediction of regional droughts using climate inputs and the SVM-copula approach. Hydrological Processes, 28(19), 4989-5009.
58. Ganguli, P., Ganguly, A. R. (2016). Space-time trends in U.S. meteorological droughts. Journal Of Hydrology-Regional Studies, 8, 235-259.
59. Garrote, L., Martin-Carrasco, F., Flores-Montoya, F., Iglesias., A. (2007). Linking Drought Indicators to Policy Actions in the Tagus Basin Drought Management Plan. Water Resources Management, 21(5), 873-882.
60. Giordano, R., Preziosi, E., Romano, E. (2013). Integration of local and scientific knowledge to support drought impact monitoring: some hints from an Italian case study. Natural Hazards, 69(1), 523-544.
61. Guzmám, D. A., Mohor, G. S., Taffarello, D., and Mendiondo, E. M. (2017). Economic impacts of drought risks for water utilities through Severity-Duration-Frequency framework under climate change scenarios. Hydrology and Earth System Sciences Discussions, https://doi.org/10.5194/hess-2017-615.
62. Halwatura, D., Lechner, A. M., Arnold, S. (2015). Design droughts: A new planning tool for ecosystem rehabilitation. International Journal of Geomate, 8(15), 1138-1142.
63. Halwatura, D., Lechner, A. M., Arnold, S. (2015). Drought severity-duration-frequency curves: a foundation for risk assessment and planning tool for ecosystem establishment in post-mining landscapes. Hydrology and Earth System Sciences, 19(2), 1069-1091.
64. Hao, Z.C., Hao, F. H., Singh, V. P., Xia, Y. L., Ouyang, W., Shen, X. Y. (2016). A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices. Advances in Water Resources, 92, 240-247.
65. Hayes, M. J., Svoboda, M. D., Wilhite D. A., Vanyarkho O. V. (1999). Monitoring the 1996 Drought Usingthe Standardized Precipitation Index. Bulletin of the American Meteorological Society, 80(3), 429-438.
66. Hayes, M. J., Svoboda, M. D., Wilhite, D. A., Vanyarkho, O. V. (1999). Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American Meteorological Society, 80(3), 429-438.
67. Hsu, C. W., Chang, C. C. & Lin, C. J. (2003). A Practical Guide to Support Vector Classification. Techincal report, Department of Computer Science and Information Engineering, National Taiwan University.
68. Huang W. C., Chou, C. C. (2007). Risk-based drought early warning system in reservoir operation. Advances in Water Resources ,31(4) , 649-660.
69. Ishak, A. M., Remesan, R., Srivastava, P. K., Islam, T., & Han, D. (2012). Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach. Water Resources Management, 27(1), 1-23.
70. Jain, A., & Kumar, A. M. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2), 585-592.
71. Jalalkamali, A., Moradi, M., & Moradi, N. (2015). Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index. International Journal of Environmental Science and Technology, 12(4), 1201-1210.
72. Komasi, M., Sharghi, S., & Safavi, H. R. (2018). Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran). Journal of Hydroinformatics, 20(4), 975-988.
73. Krause, P., Boyle, D. P., & Bäse, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89-97.
74. Kwak, J., Kim, S., Kim, D., Kim, H. (2016). Hydrological Drought Analysis Based on Copula Theory. River Basin Management, Chapter 4, Published by INTECH.
75. Lee, J. H., Kim, C. J. (2013). A multimodel assessment of the climate change effect on the drought severity-duration-frequency relationship. Hydrological Processes, 27(19), 2800-2813.
76. Lee, T. (2008). Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan. Engineering Applications of Artificial Intelligence, 21(1), 63-72.
77. Legates, D. R., & Mccabe, G. J. (1999). Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241.
78. Lin, G. F., Chen, G. R., Huang, P. Y., & Chou, Y. C. (2009a). Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. Journal of Hydrology, 372(1-4), 17-29.
79. Lin, G. F., Chen, G. R., Wu, M. C., & Chou, Y. C. (2009b). Effective Forecasting of Hourly Typhoon Rainfall Using Support Vector Machines. Water Resources Research, 45.
80. Lin, G. F., Jhong, B. C., & Chang, C. C. (2013). Development of an Effective Data-Driven Model for Hourly Typhoon Rainfall Forecasting. Journal of Hydrology, 495, 52-63.
81. Lin, G. F., Wang, T. C., & Chen, L. H. (2016). A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods. Advances in Meteorology, 7575126.
82. Linsley, R. K., Jr., M. A. Kohler, and J. C. H. Paulhus (1975). Hydrology for Engineers, 2nd ed., McGraw-Hill, New York.
83. Liong, S. Y., & Sivapragasam, C. (2002). Flood Stage Forecasting with Support Vector Machines. Journal of the American Water Resources Association, 38(1), 173-186.
84. McKee, T. B., N. J. Doesken, and J. Kleist. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, Jan17-23, 1993, Anaheim CA, pp.179-186.
85. Meng, E., Huang, S., Huang, Q., Fang, W., Wu, L., & Wang, L. (2019). A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. Journal of Hydrology, 568, 462-478.
86. Mishra, A. K., Desai, V. R. (2005). Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment, 19(5), 326-339.
87. Mishra, Ashok & Desai, V & Singh, Vijay. (2007). Drought Forecasting Using a Hybrid Stochastic and Neural Network Model. Journal of Hydrologic Engineering, 12(6), 626-638.
88. Mokhtarzad, M., Eskandari, F., Vanjani, N. J., & Arabasadi, A. (2017). Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental Earth Sciences, 76(21), 729.
89. Monira, S. S., Faisal, Z. M., & Hirose, H. (2010). Comparison of artificially intelligent methods in short term rainfall forecast. 2010 13th International Conference on Computer and Information Technology (ICCIT), Dhaka, 39-44.
90. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of The Asabe, 50(3), 885-900.
91. Morid, S., Smakhtin, V., & Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27(15), 2103-2111.
92. Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282-290.
93. Rad, A. M., Ghahraman, B., Khalili, D., Ghahremani, Z., Ardakani, S.A. (2017). Integrated meteorological and hydrological drought model: A management tool for proactive water resources planning of semi-arid regions. Advances in Water Resources, 107, 336-353.
94. Rahmat, S. N., Jayasuriya, N., Bhuiyan, M. (2015). Development of drought severity-duration-frequency curves in Victoria, Australia. Australian Journal Of Water Resources, 19(1), 31-42.
95. Razmkhah, H. (2016). Preparing stream flow drought severity–duration–frequency curves using threshold level method. Arabian Journal of Geosciences, 9(7) , UNSP 513.
96. Razmkhah, H. (2017). Comparing Threshold Level Methods in Development of Stream Flow Drought Severity-Duration-Frequency Curves. Water Resources Management, 31(13), 4045-4061.
97. Reddy, M. J., Ganguli, P. (2012). Application of copulas for derivation of drought severity-duration-frequency curves. Hydrological Processes, 26(11), 1672-1685.
98. Romano, E., Guyennon, N., Duro, A., Giordano, R., Petrangeli, A. B., Portoghese, I., Salerno, F. (2018). A Stakeholder Oriented Modelling Framework for the Early Detection of Shortage in Water Supply Systems. WATER, 10(6), UNSP 762.
99. She, D. X., Mishra, A. K., Xia, J., Zhang, L.P., Zhang, X. (2016). Wet and dry spell analysis using copulas. International Journal of Climatology, 36(1), 476-491.
100. Shiau, J. T. (2006). Fitting drought duration and severity with two-dimensional copulas. Water Resources Management, 20(5), 795-815.
101. Shiau, J. T., Modarres, R. (2009). Copula-based drought severity-duration-frequency analysis in Iran. Meteorological Applications, 16(4), 481-489.
102. Sivapragasam, C., Liong, S. Y. & Pasha, M. F. K. (2001). Rainfall and runoff forecasting with SSA-SVM approach. Journal of Hydroinformatics, 3(3), 141-152.
103. Song, S.; Singh, V. P. (2010). Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm. Stochastic Environmental Research and Risk Assessment, 24(5), 783-805.
104. Sung, J. H., Chung, E. S. (2014). Development of streamflow drought severity-duration-frequency curves using the threshold level method. Hydrology And Earth System Sciences, 18(9), 3341-3351.
105. Todisco, F., Mannocchi, F., Vergni, L. (2013). Severity-duration-frequency curves in the mitigation of drought impact: an agricultural case study. Natural Hazards, 65(3), 1863-1881.
106. Tosunoglu, F., Kisi, O. (2016). Joint modelling of annual maximum drought severity and corresponding duration. Journal of Hydrology, 543, 406-422.
107. Tu, X. J., Wu, H. O., Singh, V. P., Chen, X. H., Lin, K. R., Xie, Y. T. (2018). Multivariate design of socioeconomic drought and impact of water reservoirs. Journal of Hydrology, 566, 192-204.
108. U.S. Army Corps of Engineers (1994) Managing Water for Drought. National Study of Water Management during Drought, Institute for Water Resources, IWR Report 94-NDS-8.
109. Valipour, M. (2015). Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms. Meteorological Applications, 23(1), 91-100.
110. Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer-Verlag: New York.
111. Vapnik, V.N. (1998). Statistical Learning Theory. John Wiley & Sons: New York.
112. Vasiliades, L., Sarailidisand, G., Loukas, A. (2017). Hydrological modelling of low flows for operational water resources management. European Water, 57, 223-229.
113. Wang, W. C., Chau, K. W., Xu, D. M., Chen, X. Y. (2015). Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition. Water Resources Management, 29(8), 2655-2675.
114. Wang, W., Chau, K., Cheng, C., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3-4), 294-306.
115. Wilhite D.A., Hayes M.J., Svoboda M.D. (2000). Drought Monitoring and Assessment: Status and Trends in the United States. In: Vogt J.V., Somma F. (eds) Drought and Drought Mitigation in Europe. Advances in Natural and Technological Hazards Research, vol 14. Springer, Dordrecht.
116. Yu, P.S., Chen, S.T., Chang, I.F. (2006). Support vector regression for real-timeflood stage forecasting. Journal of Hydrology, 328(3-4), 704-716.
117. Zhang, Q., Xiao, M., Singh, V. P. (2015). Uncertainty evaluation of copula analysis of hydrological droughts in the East River basin, China. Global and Planetary Change, 129, 1-9.
118. Zhang, Q., Xiao, M., Singh, V. P., Chen, X. H. (2013). Copula-based risk evaluation of hydrological droughts in the East River basin, China. Stochastic Environmental Research and Risk Assessment, 27(6), 1397-1406.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2020-09-01起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2020-09-01起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw