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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7236完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 游景雲 | |
| dc.contributor.author | Pei-Yu Wu | en |
| dc.contributor.author | 巫佩諭 | zh_TW |
| dc.date.accessioned | 2021-05-19T17:40:28Z | - |
| dc.date.available | 2024-08-15 | |
| dc.date.available | 2021-05-19T17:40:28Z | - |
| dc.date.copyright | 2019-08-15 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-07 | |
| dc.identifier.citation | 1. Abaurrea, J., & Cebrián, A. C. (2002). Drought analysis based on a cluster Poisson model: distribution of the most severe drought. Climate Research, 22(3), 227-235.
2. Andreadis, K. M., Clark, E. A., Wood, A. W., Hamlet, A. F., & Lettenmaier, D. P. (2005). Twentieth-century drought in the conterminous United States. Journal of Hydrometeorology, 6(6), 985-1001. 3. Aussem, A., & Murtagh, F. (1997). Combining neural network forecasts on wavelet-transformed time series. Connection Science, 9(1), 113-122. 4. Aussem, A., Murtagh, F., & Clermont-ferr, B. P. (1998). A neuro-wavelet strategy for web traffic forecasting. 5. Bonaccorso, B., Cancelliere, A., & Rossi, G. (2003). An analytical formulation of return period of drought severity. Stochastic Environmental Research and Risk Assessment, 17(3), 157-174. 6. Cebrián, A. C., & Abaurrea, J. (2006). Drought analysis based on a marked cluster Poisson model. Journal of Hydrometeorology, 7(4), 713-723. 7. Chen, L., Singh, V. P., Guo, S., Mishra, A. K., & Guo, J. (2012). Drought analysis using copulas. Journal of Hydrologic Engineering, 18(7), 797-808. 8. Chen, P. C., Wang, Y. H., You, G. J. Y., & Wei, C. C. (2017). Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan. Journal of Hydrology, 545, 197-211. 9. Coles, S., Bawa, J., Trenner, L., & Dorazio, P. (2001). An introduction to statistical modeling of extreme values (Vol. 208). London: Springer. 10. Dai, A. (2011). Drought under global warming: a review. Wiley Interdisciplinary Reviews: Climate Change, 2(1), 45-65. 11. Dai, A., Trenberth, K. E., & Qian, T. (2004). A global dataset of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology, 5(6), 1117-1130. 12. Daubechies, I. (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE transactions on information theory, 36(5), 961-1005. 13. De Michele, C., & Salvadori, G. (2003). A generalized Pareto intensity‐duration model of storm rainfall exploiting 2‐copulas. Journal of Geophysical Research: Atmospheres, 108(D2). 14. Dracup, J. A., Lee, K. S., & Paulson Jr, E. G. (1980). On the definition of droughts. Water Resources Research, 16(2), 297-302. 15. Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons. 16. Favre, A. C., El Adlouni, S., Perreault, L., Thiémonge, N., & Bobée, B. (2004). Multivariate hydrological frequency analysis using copulas. Water Resources Research, 40(1). 17. Gibbs, W. J. (1967). Rainfall deciles as drought indicators. 18. González, J., & Valdés, J. B. (2003). Bivariate drought recurrence analysis using tree ring reconstructions. Journal of Hydrologic Engineering, 8(5), 247-258. 19. Guttman, N. B. (1999). Accepting the standardized precipitation index: a calculation algorithm 1. JAWRA Journal of the American Water Resources Association, 35(2), 311-322. 20. Hollinger, S. E., Isard, S. A., & Welford, M. R. (1993, January). A new soil moisture drought index for predicting crop yields. In Preprints, Eighth Conference on Applied Climatology (pp. 187-190). Anaheim, CA: American Meteorological Society. 21. Hsu, K. C., & Li, S. T. (2010). Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network. Advances in Water Resources, 33(2), 190-200. 22. Huang, N. E., Long, S. R., & Shen, Z. (1996). The mechanism for frequency downshift in nonlinear wave evolution. In Advances in Applied Mechanics (Vol. 32, pp. 59-117C). Elsevier. 23. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. 24. Katz, R. W. (2013). Statistical methods for nonstationary extremes. In Extremes in a changing climate (pp. 15-37). Springer, Dordrecht. 25. Keyantash, J., & Dracup, J. A. (2002). The quantification of drought: an evaluation of drought indices. Bulletin of the American Meteorological Society, 83(8), 1167-1180. 26. Kim, T. W., & Valdés, J. B. (2003). Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8(6), 319-328. 27. Kim, T. W., Valdés, J. B., & Yoo, C. (2003). Nonparametric approach for estimating return periods of droughts in arid regions. Journal of Hydrologic Engineering, 8(5), 237-246. 28. Lee, T., Modarres, R., & Ouarda, T. B. (2013). Data‐based analysis of bivariate copula tail dependence for drought duration and severity. Hydrological Processes, 27(10), 1454-1463. 29. Liu, W. T., & Kogan, F. N. (1996). Monitoring regional drought using the vegetation condition index. International Journal of Remote Sensing, 17(14), 2761-2782. 30. Loaiciga, H. A., & Leipnik, R. B. (1996). Stochastic renewal model of low-flow streamflow sequences. Stochastic Hydrology and Hydraulics, 10(1), 65-85. 31. Ma, M., Song, S., Ren, L., Jiang, S., & Song, J. (2013). Multivariate drought characteristics using trivariate Gaussian and Student t copulas. Hydrological Processes, 27(8), 1175-1190. 32. McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179-183). Boston, MA: American Meteorological Society. 33. Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of hydrology, 391(1-2), 202-216. 34. Mishra, A. K., & Singh, V. P. (2011). Drought modeling–A review. Journal of Hydrology, 403(1-2), 157-175. 35. Mishra, A. K., Singh, V. P., & Desai, V. R. (2009). Drought characterization: a probabilistic approach. Stochastic Environmental Research and Risk Assessment, 23(1), 41-55. 36. Nalbantis, I., & Tsakiris, G. (2009). Assessment of hydrological drought revisited. Water Resources Management, 23(5), 881-897. 37. Obasi, G. O. P. (1994). WMO's role in the international decade for natural disaster reduction. Bulletin of the American Meteorological Society, 75(9), 1655-1662. 38. Palmer, W. C. (1965). Meteorological droughts. US Department of Commerce Weather Bureau Research Paper 45, 58 pp. 39. Palmer, W. C. (1968). Keeping track of crop moisture conditions, nationwide: The new crop moisture index. 40. Papoulis, A. (1991). Probability, Random Variables, and Stochastic Processes, 3rd ed., New York: McGraw-Hill. 41. Parry, S., Wilby, R. L., Prudhomme, C., & Wood, P. J. (2016). A systematic assessment of drought termination in the United Kingdom. Hydrology and Earth System Sciences, 2016, Vol.20(10), p.4265-4281 42. Salas, J. D., Fu, C., Cancelliere, A., Dustin, D., Bode, D., Pineda, A., & Vincent, E. (2005). Characterizing the severity and risk of drought in the Poudre River, Colorado. Journal of Water Resources Planning and Management, 131(5), 383-393. 43. Saravanan, N., Siddabattuni, V. K., & Ramachandran, K. I. (2008). A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box. Expert Systems with Applications, 35(3), 1351-1366. 44. Shafer, B. A. (1982). Developemnet of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. In Proceedings of the 50th Annual Western Snow Conference, Colorado State University, Fort Collins, 1982. 45. Shiau, J. T. (2006). Fitting drought duration and severity with two-dimensional copulas. Water Resources Management, 20(5), 795-815. 46. Shiau, J. T., & Modarres, R. (2009). Copula‐based drought severity‐duration‐frequency analysis in Iran. Meteorological Applications, 16(4), 481-489. 47. Shiau, J. T., & Shen, H. W. (2001). Recurrence analysis of hydrologic droughts of differing severity. Journal of Water Resources Planning and Management, 127(1), 30-40. 48. Shiau, J. T., Feng, S., & Nadarajah, S. (2007). Assessment of hydrological droughts for the Yellow River, China, using copulas. Hydrological Processes: An International Journal, 21(16), 2157-2163. 49. Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publ. inst. statist. univ. Paris, 8, 229-231. 50. Song, S., & Singh, V. P. (2010a). Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data. Stochastic Environmental Research and Risk Assessment, 24(3), 425-444. 51. Song, S., & Singh, V. P. (2010b). Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm. Stochastic Environmental Research and Risk Assessment, 24(5), 783-805. 52. Stitou, Y., Lasmar, N., & Berthoumieu, Y. (2009). Copulas based multivariate gamma modeling for texture classification. In 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1045-1048). IEEE. 53. Strupczewski, W. G., & Kaczmarek, Z. (2001b). Non-stationary approach to at-site flood frequency modelling II. Weighted least squares estimation. Journal of Hydrology, 248(1-4), 143-151. 54. Strupczewski, W. G., Singh, V. P., & Feluch, W. (2001a). Non-stationary approach to at-site flood frequency modelling I. Maximum likelihood estimation. Journal of Hydrology, 248(1-4), 123-142. 55. Strupczewski, W. G., Singh, V. P., & Mitosek, H. T. (2001c). Non-stationary approach to at-site flood frequency modelling. III. Flood analysis of Polish rivers. Journal of Hydrology, 248(1-4), 152-167. 56. Szalai, S., Szinell, C. S., & Zoboki, J. (2000). Drought monitoring in Hungary. Early warning systems for drought preparedness and drought management, 57, 182-199. 57. Tabari, H., Nikbakht, J., & Talaee, P. H. (2013). Hydrological drought assessment in Northwestern Iran based on streamflow drought index (SDI). Water Resources Management, 27(1), 137-151. 58. Tawn, J. A. (1988). An extreme-value theory model for dependent observations. Journal of Hydrology, 101(1-4), 227-250. 59. Wilhite, D. A. (2000). Drought as a natural hazard: concepts and definitions. 60. Wilhite, D. A., & Glantz, M. H. (1985). Understanding: the drought phenomenon: the role of definitions. Water International, 10(3), 111-120. 61. Wong, G., Lambert, M. F., Leonard, M., & Metcalfe, A. V. (2009). Drought analysis using trivariate copulas conditional on climatic states. Journal of Hydrologic Engineering, 15(2), 129-141. 62. Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(01), 1-41. 63. Yevjevich, V. M. (1967). Objective approach to definitions and investigations of continental hydrologic droughts, An. Hydrology papers (Colorado State University); no. 23. 64. Zelenhasić, E., & Salvai, A. (1987). A method of streamflow drought analysis. Water Resources Research, 23(1), 156-168. 65. Zhang, B. L., & Dong, Z. Y. (2001). An adaptive neural-wavelet model for short term load forecasting. Electric Power Systems Research, 59(2), 121-129. 66. Zhang, L. S. V. P., & Singh, V. P. (2006). Bivariate flood frequency analysis using the copula method. Journal of Hydrologic Engineering, 11(2), 150-164. 67. Zheng, K., Zhongzhen, J., & Yihua, L. (2000). Wavelet Analysis of Rainfall Data in North China [J]. Climatic and Environmental Research, 3. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7236 | - |
| dc.description.abstract | 乾旱是一種經常性與反覆出現的氣候現象。由於近年來氣候變遷與全球對於水資源需求的增加，我們經歷了更嚴重的乾旱。為了評估乾旱趨勢的影響，本研究旨在提出一個完善的分析架構，利於流量乾旱分析並模擬未來情境的乾旱特徵。基於本研究提出的關聯結構(Copulas)與卜松過程(Poisson process)之乾旱模型架構。首先，分析歷史水庫入流數據，估算最佳乾旱事件之參數，並定義乾旱事件。接著從乾旱事件中萃取三個乾旱特徵，分別為乾旱持續時間，乾旱缺水量以及乾旱發生時間。此三個乾旱特徵具有一定相關性，故本研究使用三維高斯關聯結構，建構隨機模型來模擬乾旱事件之發生，此外，為了更恰當地模擬乾旱事件，本研究進一步採用改良之卜松過程來描述兩乾旱事件間之間隔年份，此做法能有效地降低合成乾旱事件時的困難度。至此，上述分析過程與模擬結果皆是基於定常性假設，而為進一步考慮氣候變化的影響並分析乾旱特徵的趨勢，本研究於最後提出一個非定常性乾旱分析架構，能繁衍可信之未來乾旱事件。這項研究的結果可以應用在施政方針的參考、提供未來的乾旱政策或乾旱調節模式的檢驗，並在後續中、長期有更好的規劃。 | zh_TW |
| dc.description.abstract | Drought is a regular and recurring climatic phenomenon. Because of climate change and increasing water demand, we have experienced higher drought severity in recent years. To evaluate the impact of the trend of drought, this study aims to develop an improved framework for streamflow drought analysis and simulation of future conditions. For our proposed framework based on Copula and Poisson process, this study firstly analyzes historical inflow data to estimate statistical parameters of drought. Secondly, I defined three drought indices: drought duration, drought deficit, and the occurrence time of a drought event. It can be found that these three indices are found to be correlated. Afterwards, the marginal distributions of these indices are estimated. Accordingly, this study constructs a stochastic model using a three-dimensional Gaussian copula to simulate the occurrence of drought events. In addition, to more appropriately simulate drought occurrences, Poisson process is applied to describe recurrence year which is the integral number of years between two drought events. Until now the simulation of drought events is under the stationary assumption in current stage. Therefore, a consideration of the impact of climate change on drought analysis is included in this study as well. The randomly synthetic droughts with several possibilities are generated by the proposed robust non-stationary drought model. The results of this work are valuable and can be utilized to support the decision or policy making processes in drought management. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:40:28Z (GMT). No. of bitstreams: 1 ntu-108-R06521303-1.pdf: 3228939 bytes, checksum: 234d302f53332ac939efeb39561efa0b (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS vi Chapter 1 Introduction 1 Chapter 2 Literature Review 3 2.1 Drought definition 3 2.2 Drought characteristics 8 2.3 Stochastic model, Copulas and Poisson process 10 2.4 The analysis of climate change 12 Chapter 3 Methodology 16 3.1 Drought definition 17 3.1.1 Standardized Precipitation Index (SPI) 21 3.1.2 The selection of parameters 23 3.2 Drought characteristics 26 3.3 Copulas and Poisson process 28 3.3.1 Copulas 29 3.3.3 Poisson process 34 3.4 Non-stationarity 36 Chapter 4 Case study 39 4.1 Study area and dataset 39 4.2 Results and discussion 40 4.2.1 Drought definition 40 4.2.2 Drought characteristics and the univariate distributions 47 4.2.3 Copula model 52 4.2.4 Synthetic droughts 54 4.2.5 Non-stationary analysis 55 4.2.6 Synthetic droughts under non-stationary scenario 58 Chapter 5 Conclusions and Future work 61 5.1 Conclusions 61 5.2 Future work 63 REFERENCES 64 | |
| dc.language.iso | en | |
| dc.title | 結合關聯結構與卜松過程之乾旱分析架構 | zh_TW |
| dc.title | A Drought Analysis Framework based on Copula and
Poisson Process | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 胡明哲,陳佳正,孫建平 | |
| dc.subject.keyword | 乾旱分析,關聯結構,卜松過程,非定常性, | zh_TW |
| dc.subject.keyword | Drought analysis,Copula,Poisson process,Non-stationarity, | en |
| dc.relation.page | 68 | |
| dc.identifier.doi | 10.6342/NTU201902777 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2019-08-08 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-08-15 | - |
| 顯示於系所單位： | 土木工程學系 | |
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