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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 游景雲 | |
dc.contributor.author | Kuan-Cheng Fu | en |
dc.contributor.author | 傅冠禎 | zh_TW |
dc.date.accessioned | 2021-06-17T02:51:00Z | - |
dc.date.available | 2018-08-25 | |
dc.date.copyright | 2017-08-25 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-15 | |
dc.identifier.citation | Aksoy, H., & Bayazit, M. (2000). A model for daily flows of intermittent streams. Hydrological Processes, 14(10), 1725-1744.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69083 | - |
dc.description.abstract | 隨著都市化的發展以及氣候變遷的影響,乾旱及豪大雨等極端氣候發生的強度與頻率逐漸增加,並預期會對於環境造成更大之衝擊,因此也更加彰顯都市排水及防洪議題之重要性。而防洪措施之施作以及洪水管理方法之建立,皆仰賴於降雨序列作為輸入之參數,但由於測站歷史資料取得不易,且常有資料長度不足之問題,因此推估降雨量於時間與空間上之分佈逐漸成為一重要課題。
水文學家過往基本上專注於發展單一測站之降雨序列模擬,較少著重於降雨之空間分布特性,因此本研究將基於非參數方法之精神,提供多測站之降雨序列模擬之方法。本研究方法主要分為三部分,首先藉由馬可夫序列判釋空間中各測站是否發生降雨;第二部分則假定某站於時間 下之降雨量與時間 下之各站降雨量存在線性關係,並利用該線性關係建立多變數自回歸模式,若測站發生降雨則進一步利用此自回歸模式估計降雨量;最後則根據改進之最近鄰居法處理自回歸模式中之殘差項,藉以提升雨量序列之多樣性。最後此研究方法將基於歷史資料之特性,針對淡水河流域中三十五個測站進行降雨序列之模擬。 | zh_TW |
dc.description.abstract | With the change of future climatic conditions, the environment will face more severe hydrological events. Under these situations, urban flooding is one of serious emerging problems. For this concern, simulating spatial-temporal distribution of precipitation has become an important issue.
During the past decades, hydrologists have improved the development of generating synthetic sequences of precipitation in a given rain gauge, but seldom focused on the spatial characteristics of precipitation in a given space region. Therefore, the aim of this study is to extend the concept of nonparametric approaches to develop a framework which is capable of simultaneously generating multisite precipitation amounts in a given space region over time. The purposed framework is composed of three parts: multisite precipitation occurrence process, multisite precipitation amounts process, and modified K-NN method. First, a first-order Markov chain is used to simultaneously simulate precipitation occurrences across the network of multiple gauges. Subsequently, the correlations between precipitation amount of the given rain gauge and precipitation amounts of all rain gauges across the network are obtained by a multivariate autoregressive model. Moreover, the concepts of spatial uncertainty, based on the residual sampling in the modified K-NN method, are added to the model but moderately adjusted to deal with the change of the correlation functions. After that, a case study is demonstrated in Danshui River Basin with the historical daily precipitation data from thirty-five rain gauges for seven-year period. Besides, the simulation results of the purposed framework are expected to preserve the statistical properties of the observed data and also possess a rich variety in time sequences. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T02:51:00Z (GMT). No. of bitstreams: 1 ntu-106-R04521312-1.pdf: 4846224 bytes, checksum: 659cd6c3390fb5521f6e0778fc14942a (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 i
中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Background 1 1.2 Objectives 2 1.2 Framework 3 Chapter 2 Literature Review 5 2.1 Hydrologic simulation at a single station 5 2.2 Space and time model of precipitation 7 2.3 The nonparametric techniques 9 Chapter 3 Methodology 11 3.1 Study area and data source 11 3.2 Multisite precipitation occurrence process 14 3.2.1 A first-order, two-state Markov chain for a single rain gauge 14 3.2.2 A first-order, multi-state Markov chain for multiple rain gauges 16 3.3 Multisite precipitation amounts process 18 3.3.1 Multivariate autoregressive model (Ⅰ) 18 3.3.2 Multivariate autoregressive model (Ⅱ) 19 3.3.3 Multivariate autoregressive model (Ⅲ) 20 3.3.3 Discussion of the number of independent variables 22 3.4 Spatial uncertainty 27 3.4.1 Modified K-nearest neighbor (K-NN) method 27 3.4.2 The application of the modified K-NN method 29 3.5 Statistical tests 31 3.4.1 Wilcoxon rank-sum test 31 3.4.2 Two-sample Kolmogorov-Smirnov test 32 Chapter 4 Results 34 4.1 Simulated precipitation occurrences 34 4.2 The empirical distribution functions 39 4.3 The correlation coefficients 49 4.4 The results of the statistical tests 52 Chapter 5 Conclusions and Recommendations 57 5.1 Conclusions 57 5.2 Recommendations 58 REFERENCE 60 | |
dc.language.iso | en | |
dc.title | 區域降雨模擬時空架構發展應用 | zh_TW |
dc.title | Development of Spatio-temporal Framework for Regional Precipitation Simulation | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 孫建平,陳憲宗,魏志強 | |
dc.subject.keyword | 日雨量模擬,時空架構,馬可夫鏈,多變數自回歸模式,最近鄰居法, | zh_TW |
dc.subject.keyword | daily precipitation simulation,spatial-temporal framework,Markov chain,multivariate autoregressive model,K-nearest neighbor method, | en |
dc.relation.page | 62 | |
dc.identifier.doi | 10.6342/NTU201703205 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-08-15 | |
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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