請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60971完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 游景雲 | |
| dc.contributor.author | Po-Chun Chen | en |
| dc.contributor.author | 陳柏均 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:39:10Z | - |
| dc.date.available | 2013-08-14 | |
| dc.date.copyright | 2013-08-14 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-13 | |
| dc.identifier.citation | 1. Bier, V. M., et al. (1999). 'A survey of approaches for assessing and managing the risk of extremes.' Risk analysis 19(1): 83-94.
2. Cave, B. M. and K. Pearson (1914). 'Numerical illustrations of the variate difference correlation method.' Biometrika 10(2/3): 340-355. 3. Coles, S., et al. (2003). 'A fully probabilistic approach to extreme rainfall modeling.' Journal of Hydrology 273(1): 35-50. 4. Cryer, J. D. and K.-S. Chan (2008). Time series analysis with applications in R, Springer. 5. Cunderlik, J. M. and D. H. Burn (2003). 'Non-stationary pooled flood frequency analysis.' Journal of Hydrology 276(1): 210-223. 6. Huang, N. E., et al. (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. 7. Kallache, M., et al. (2005). 'Trend assessment: applications for hydrology and climate research.' Nonlinear Processes in Geophysics 12(2): 201-210. 8. Katz, R. W. and B. G. Brown (1992). 'Extreme events in a changing climate: variability is more important than averages.' Climatic Change 21(3): 289-302. 9. Katz, R. W., et al. (2005). 'Statistics of extremes: Modeling ecological disturbances.' Ecology 86(5): 1124-1134. 10. Katz, R. W., et al. (2002). 'Statistics of extremes in hydrology.' Advances in Water Resources 25(8): 1287-1304. 11. Khaliq, M., et al. (2009). 'Identification of hydrological trends in the presence of serial and cross correlations: A review of selected methods and their application to annual flow regimes of Canadian rivers.' Journal of Hydrology 368(1): 117-130. 12. Khaliq, M., et al. (2006). 'Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review.' Journal of Hydrology 329(3): 534-552. 13. Koutsoyiannis, D. (2004). 'Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation/Statistiques de valeurs extremes et estimation de precipitations extremes: I. Recherche theorique.' Hydrological Sciences Journal 49(4). 14. Liu, Q., et al. (2008). 'Spatial and temporal variability of annual precipitation during 1961–2006 in Yellow River Basin, China.' Journal of Hydrology 361(3): 330-338. 15. McCuen, R. H. (2002). Modeling hydrologic change: statistical methods, CRC press. 16. Mileti, D. (1999). Disasters by design: A reassessment of natural hazards in the United States, National Academies Press. 17. Milly, P., et al. (2007). 'Stationarity is dead.' Ground Water News & Views 4(1): 6-8. 18. Mutua, F. M. (1994). 'The use of the Akaike information criterion in the identification of an optimum flood frequency model.' Hydrological Sciences Journal 39(3): 235-244. 19. North, M. (1980). 'Time-dependent stochastic model of floods.' Journal of the Hydraulics Division 106(5): 649-665. 20. Olsen, J. R., et al. (1998). 'Risk of extreme events under nonstationary conditions.' Risk Analysis 18(4): 497-510. 21. Sang, Y.-F. (2012). 'A practical guide to discrete wavelet decomposition of hydrologic time series.' Water Resources Management 26(11): 3345-3365. 22. Strupczewski, W. and W. Feluch (1997). System of identification of an optimum flood frequency model with time dependent parameters (IDT). Integrated Approach to Environmental Data Management Systems, Springer: 291-300. 23. Strupczewski, W., et al. (2001). 'Non-stationary approach to at-site flood frequency modelling I. Maximum likelihood estimation.' Journal of Hydrology 248(1): 123-142. 24. Strupczewski, W., et al. (2001). 'Non-stationary approach to at-site flood frequency modelling. III. Flood analysis of Polish rivers.' Journal of Hydrology 248(1): 152-167. 25. Strupczewski, W. G. and Z. Kaczmarek (2001). 'Non-stationary approach to at-site flood frequency modelling II. Weighted least squares estimation.' Journal of Hydrology 248(1): 143-151. 26. Torrence, C. and G. P. Compo (1998). 'A practical guide to wavelet analysis.' Bulletin of the American Meteorological Society 79(1): 61-78. 27. Villarini, G., et al. (2009). 'Flood frequency analysis for nonstationary annual peak records in an urban drainage basin.' Advances in Water Resources 32(8): 1255-1266. 28. Wigley, T. M. (2009). 'The effect of changing climate on the frequency of absolute extreme events.' Climatic Change 97(1-2): 67-76. 29. Wu, Z. and N. E. Huang (2009). 'Ensemble empirical mode decomposition: a noise-assisted data analysis method.' Advances in Adaptive Data Analysis 1(01): 1-41. 30. Zheng, Y., et al. (2001). 'Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: theory and application.' Signal Processing 81(9): 1823-1848. 31. 游保杉、楊道昌 (1992)，「三參數極端值分佈於水文頻率分析之應用(年最大日暴雨)」，台灣水利季刊，第四十卷，第二期，第36-45頁。 32. 台灣省水利局 (1989)，「臺灣水文資料電腦檔應用之研究」。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60971 | - |
| dc.description.abstract | 臺灣近年來極端降雨事件日益頻繁，降雨分布有更加集中和強度增大的趨勢，以往傳統水文頻率分析多基於年最大水文序列一致性與獨立性的先驗假設，但全球暖化與土地利用等人為因素所造成氣候變遷的影響，使得此基本假設的適用性有所質疑，基於上述理由，以往傳統頻率工具所設計的結果可靠度需進一步的檢視，應提出非定常性之頻率分析架構以供因應未來之規劃所需。
有別於國內研究大多僅分析第一階動差之非定常性，本研究以分配與趨勢鑑定（Identification of distribution and trend）原則分析臺灣地區九組具代表性的氣象測站（基隆、臺北、新竹、臺中、臺南、高雄、恆春、花蓮以及臺東）日雨量之年極大值，並以Akaike information criteria為依據，決定最佳模型。另外導入其它趨勢估計方法比較結果差異，除了原本的加權最小平方估計，再進行討論離散小波轉換與總和經驗模態分解等共三種理論基礎相異方法。 研究結果發現若僅以線性模型假設趨勢函數，則三種估計方法所得到之結果相同，皆認定基隆、臺東和花蓮三站存在非定常性，此外根據配適的最佳模型，估算各站未來極端雨量之10、20、30和40年的重現期距改變量，用以檢討非定常性對於傳統回歸週期觀念的影響，考慮時間風險增量的概念，檢視改變量可以發現在此案例中，回歸週期與平均等待時間之衰退量相當，但是後者定義更適合應用在實務上，另外也顯示加權最小平方估計所得到之回歸週期相較於另外兩種估計法更為保守。 | zh_TW |
| dc.description.abstract | Due to the climate changing, the hydrological stationarity, a fundamental component of engineering design and practice involves predicting or characterizing future conditions based on previous observation or record, could be inappropriate. We have been experiencing more intense and more frequent extreme hydrological events in recent. Under current climate changing condition, the stationary assumption and corresponding assessment approach need to be re-evaluated carefully. This study investigates the nonstationarity of annual maximum daily precipitation in Taiwan. Based on the concept of IDT (identification of distribution and trend), three different schemes are applied to analyze the precipitation data from nine major cities in Taiwan. These studies adopts, Weighted Least Square Method, Discrete Wavelet Transform Method, and Empirical Mode Decomposition, to explore the time variation of first and second statistical moments of annual maximum precipitation. From the analysis, we find that all the three schemes demonstrate clear nonstationarity in Keelung, Taitung and Hwalian. According the result, this study further discusses the change of exceedance probability and return period in the near future. As results, we can determine the hydrological risks, review the current management policies and engineering standards, and have a better long term planning in engineering. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:39:10Z (GMT). No. of bitstreams: 1 ntu-102-R00521304-1.pdf: 6856167 bytes, checksum: 5f33f6b8cb139286c1ebcfd2382c2ca5 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員審定書 I
誌謝 II 摘要 III Abstract IV 目錄 V 圖目錄 VII 表目錄 IX 第一章、緒論 1 1.1 研究動機 1 1.2 研究目的 3 1.3 研究流程 5 1.4 章節介紹 6 第二章、文獻回顧 7 2.1 非定常性條件下的頻率分析 7 2.2 時間序列分析 10 2.3 極端值風險 12 第三章、研究方法 15 3.1 分配與趨勢鑑定 15 3.2 時依動差估計 15 3.2.1 加權最小平方估計 16 3.2.2 離散小波轉換 20 3.2.3 總和經驗模態分解 23 3.3 競爭模型揀選 27 3.3.1 極端值一型分配 28 3.3.2 對數常態分配 28 3.3.3 皮爾森三型分配 29 第四章、結果分析 30 4.1 資料收集 30 4.2 各站時依動差分析 32 4.2.1 基於加權最小平方估計的趨勢鑑定法 34 4.2.2 離散小波轉換 44 4.2.3 總和經驗模態分解 53 4.3 方法比較 61 4.4 非定常性條件下的風險 64 第五章、結論與建議 69 5.1 結論 69 5.2 建議 70 參考文獻 71 附錄（APPENDIX） 74 A.1 加權最小平方估計 74 A.2 離散小波轉換 88 A.3 總和經驗模態分解 96 A.4 非定常性條件下的回歸週期 104 | |
| dc.language.iso | zh-TW | |
| dc.subject | 回歸週期 | zh_TW |
| dc.subject | 非定常性條件 | zh_TW |
| dc.subject | 水文頻率分析 | zh_TW |
| dc.subject | Nonstationarity | en |
| dc.subject | Hydrologic frequency analysis | en |
| dc.subject | Return period | en |
| dc.title | 非定常性水文頻率分析方法之比較探討：以台灣地區年最大日降雨為例 | zh_TW |
| dc.title | A Comparison of Methods for Non-Stationary Hydrologic Frequency Analysis: Case Study with Annual Maximum Daily recipitation in Taiwan | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李天浩,余化龍,陳憲宗,楊智傑 | |
| dc.subject.keyword | 非定常性條件,水文頻率分析,回歸週期, | zh_TW |
| dc.subject.keyword | Nonstationarity,Hydrologic frequency analysis,Return period, | en |
| dc.relation.page | 108 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-13 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-102-1.pdf 未授權公開取用 | 6.7 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。