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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 游景雲(Gene Jiing-Yun You) | |
| dc.contributor.author | Shu-Ting Hsu | en |
| dc.contributor.author | 許淑婷 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:21:15Z | - |
| dc.date.available | 2021-07-23 | |
| dc.date.available | 2022-11-23T09:21:15Z | - |
| dc.date.copyright | 2021-07-23 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-19 | |
| dc.identifier.citation | Akaike, H. A New look at the statistical model identification. IEEE Transactions on Automatic Control. (1974) 19(6): 716-723 DOI: 10.1109/TAC.1974.1100705 Panagiotelis, A., Czado, C., Joe, H. Pair Copula Constructions for Multivariate Discrete Data. Journal of the American Statistical Association. Vol 107, 2012, Issue 499, p.1063-1072. https://doi.org/10.1080/01621459.2012.682850 Sharma, A. Goyal, M.K. Bayesian networks for monthly rainfall forecast: a comparison of K2 and MCMC algorithm. International Journal of Computers and Applications, 38, 199-206,2016. Aurelius, A., Zilko, Kurowicka, D. Copula in a multivariate mixed discrete-continuous model. Computational Statistics and Data Analysis. 103(2016)28-55. https://doi.org/10.1016/j.csda.2016.02.017 Aurelius, A., Zilko, Kurowicka, D., Hanea, A. M., Rob, M.P. Goverde. The Copula Bayesian Network with mixed discrete and continuous nodes to forecast railway disruption lengths. 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A copula-based joint deficit index for droughts. Journal of Hydrology. Vol 380, Issues 1–2, (2010), p.121-134. https://doi.org/10.1016/j.jhydrol.2009.10.029 Sklar, A. Fonctions de Répartition à n Dimensions et Leurs Marges. Publications de l’Institut Statistique de l’Université de Paris, 8, 229-231. (1959) Smail, L. (2018). Bayesian network model for temperature forecasting in Dubai. AIP Conference Proceedings, 2025, 100006. https://doi.org/10.1063/1.5064935 Tarboton, D.G., A. Sharma, and U. Lall (1998), Disaggregation procedures for stochastic hydrology based on nonparametric density estimation, Water Resources Research, 34(1), 107-119. Bedford, T. Cooke, R.M. A NEW GRAPHICAL MODEL FOR DEPENDENT RANDOM VARIABLES. Ann. Statist. 30 (4) 1031 - 1068, 2002. https://doi.org/10.1214/aos/1031689016 Bedford, T. Cooke, R.M. Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines. Annals of Mathematics and Artificial Intelligence. Vol 32, p.245–268, (2001). https://doi.org/10.1023/A:1016725902970 Erhardt, T.M., Czado, C., Schepsmeier, U. R‐vine models for spatial time series with an application to daily mean temperature. Biometrics. Vol 71, Issue 2 p. 323-332. (2015) https://doi.org/10.1111/biom.12279 Ng, W.W. Panu, U.U. Comparisons of traditional and novel stochastic models for the generation of daily precipitation occurrences. Journal of Hydrology, 380 (1–2) (2010), pp. 222-236. https://doi.org/10.1016/j.jhydrol.2009.11.002 Zheng, Y. Zhu, J. Markov Carlo Monte Carlo for a Spatial-Temporal Autologistic Regression Model. Journal of Computational and Graphical Statistics, 17 (1), (2008). https://doi.org/10.1198/106186008X289641 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80017 | - |
| dc.description.abstract | 近年來，由於氣候持續變化的趨勢，異常降雨、洪水以及乾旱等極端水文事件發生頻率也日益增加。在台灣，降雨時空分布不均是很重要的議題，因為它影響了水資源的儲存和水資源的利用。隨著用水需求增加和異常氣候使台灣人民飽受乾旱之苦。降雨強度增強和降雨延時縮短也會帶來很大的洪峰流量或使洪峰來臨時間提前，造成對生命財產及經濟的嚴重影響。因此，人們開始對氣象預測的準確性越來越關心。目前為止，水文學家們對於頻率分析通常聚焦在時間分布或空間分布，鮮少關注同時考慮時間空間分布的降雨模式。 本文的研究目的是提出一種基於藤蔓關聯結構的模型的架構，本文將利用此架構模擬位於台北的十個測站降雨是否發生的時間序列。在這篇研究當中，首先利用改良的最近鄰居法，用七年的歷史日降雨量資料隨機產生降雨資料，並將日降雨量低於0.8mm者視為未發生降雨，反之，日降雨量大於0.8mm時，該日定義為有發生降雨。接下來，應用藤蔓關聯結構方法建立模型來模擬產生降雨發生的時間序列。除此之外，還應用了貝氏網路方法，同樣地產生降雨發生的時間序列，用來和藤蔓關聯結構方法比較。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:21:15Z (GMT). No. of bitstreams: 1 U0001-1907202110491700.pdf: 3137030 bytes, checksum: c60dca1dc10ab2d8f1ef6116b8c457f8 (MD5) Previous issue date: 2021 | 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 LIST OF FIGURES viii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Background 1 1.2 Objective 2 1.3 Framework 3 Chapter 2 Literature Review 5 2.1 Hydrologic Simulation 5 2.1.1 Markov chain model…………………………………………………….6 2.2 Bayesian Networks Method 8 2.3 Vine Copula Method 9 2.3.1 Copula…………………………………………………………………...9 2.3.2 Vine Copula…………………………………………………………….11 Chapter 3 Methodology 13 3.1 Pair Copula Constructions(PCCs) 13 3.1.1 Continuous data in PCCs………………………………………………16 3.1.2 Discrete data in PCCs…………………………………………………..18 3.1.3 Mixed discrete-continuous data in PCCs………………………………20 3.2 D-Vines 23 3.2.1 Definition of D-vine……………………………………………………23 3.2.2 Construction of D-vine…………………………………………………24 3.2.3 joint density of D-vine…………………………………………………25 3.3 Parameter Estimation 27 3.4 Bayesian networks 30 3.5 Stochastic Rainfall Data 34 3.5.1 Modified k-nearest neighbor bootstrap method………………………..35 3.5.2 k-nearest neighbor based disaggregation framework………………….37 3.6 Statistic Test 40 3.6.1 Kolmogorov-Smirnov Test…………………………………………….40 3.6.2 Test of Proportions……………………………………………………..41 Chapter 4 Results and Discussions 43 4.1 Study Area 43 4.2 Results and Discussions 45 4.2.1 Simulated rainfall amount by modified k-NN method…………………45 4.2.2 Precipitation occurrence simulated by vine copula model……………..49 Chapter 5 Conclusions and suggestions 59 5.1 Conclusions 59 5.2 Suggestions 60 REFERENCES 61 | |
| dc.language.iso | en | |
| dc.subject | 時空模擬架構 | zh_TW |
| dc.subject | 多站日降雨發生模擬 | zh_TW |
| dc.subject | 藤蔓關聯結構方法 | zh_TW |
| dc.subject | 貝氏網路 | zh_TW |
| dc.subject | 改良最近鄰居法 | zh_TW |
| dc.subject | spatial-temporal simulated framework | en |
| dc.subject | multisite daily precipitation occurrence simulation | en |
| dc.subject | vine copula method | en |
| dc.subject | Bayesian Networks | en |
| dc.subject | modified k-NN method | en |
| dc.title | 基於藤蔓關聯結構之多站日降雨發生序率架構 | zh_TW |
| dc.title | Stochastic Framework for Multisite Daily Rainfall Occurrences Based on Vine Copula | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 胡明哲(Hsin-Tsai Liu),孫建平(Chih-Yang Tseng),陳憲宗 | |
| dc.subject.keyword | 藤蔓關聯結構方法,貝氏網路,改良最近鄰居法,時空模擬架構,多站日降雨發生模擬, | zh_TW |
| dc.subject.keyword | vine copula method,Bayesian Networks,modified k-NN method,spatial-temporal simulated framework,multisite daily precipitation occurrence simulation, | en |
| dc.relation.page | 67 | |
| dc.identifier.doi | 10.6342/NTU202101559 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-07-20 | |
| 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 | |
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