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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭克聲 | |
dc.contributor.author | Le-Ting Lin | en |
dc.contributor.author | 林樂庭 | zh_TW |
dc.date.accessioned | 2021-05-13T08:36:56Z | - |
dc.date.available | 2017-08-24 | |
dc.date.available | 2021-05-13T08:36:56Z | - |
dc.date.copyright | 2016-08-24 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-04 | |
dc.identifier.citation | [1]經濟部石門水庫設計委員會.石門水庫工程定案計劃報告,44
[2]Cheng, K.-S., Hou, J.-C., Liou, J.-J., Wu, Y.-C., & Chiang, Jie-Lun. (2010). Stochastic simulation of bivariate gamma distribution: a frequency-factor based approach. Stochastic Environmental Research and Risk Assessment, 25(2), 107–122. [3]Chou, F. N.-F., & Wu, C.-W. (2014). Determination of cost coefficients of a priority-based water allocation linear programming model ─ a network flow approach. Hydrology and Earth System Sciences, 18(5), 1857–1872. [4]Huang, W.-C., Chu, S.-C., & Huang, P.-J. (2000). Application of simulation method on reservoir operation. Journal of Taiwan Water Conservancy, 48(4), 469–483. [5]Wilhite, D. A., and Glantz, M. H. Understanding: the drought phenomenon: the role of definitions. Water International 10, 3 (1985), 111--120. [6]Wilhite, D. A., & Glantz, M. H. (1985). Understanding: the drought phenomenon: the role of definitions. Water International, 10(3), 111–120. [7]Yerramreddy, A. R., & Wurbs, R. A. (1996). Water resources allocation based on network flow programming. Civil Engineering Systems, 13(1), 75–87. [8]Yue, S., Ouarda, T. B. M. J., & Bobée, B. (2001). A review of bivariate gamma distributions for hydrological application. Journal of Hydrology, 246(1–4), 1–18. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3800 | - |
dc.description.abstract | Reservoirs are operated and managed based on a set of rule curves. Given an ob- served time series of reservoir inflows, optimal reservoir operations can be determined by linear programming or other deterministic techniques of operation research. However, reservoir inflows are inherently stochastic and can be characterized by a random process. Thus, given a set of rule curves and a deterministic optimization technique, reservoir performance can still vary from one year to the next. In this study, we propose a stochastic approach for probabilistic risk assessment of reservoir operations for the Shihmen Reservoir in northern Taiwan. The approach consists of a stochastic reservoir inflow simulation model and a deterministic optimal reservoir operation technique, the network flow model. The stochastic reservoir inflow simulation model considers daily flows induced by ty- phoons and non-typhoon daily flows separately. Occurrences of typhoons are modeled by a Poisson process, and durations and event-total flows of typhoons are characterized by a bivariate gamma distribution. Log-transformed non-typhoon daily flows are modeled by an ARMA(1,1) time series process. Ten thousand realizations of annual daily flow series were simulated. The deterministic optimal reservoir operation was then applied to each of the 10,000 simulated annual series. Finally, a probabilistic risk assessment of reservoir operations was conducted using results of the 10,000 sets of optimal operations. It was found that the risk of agricultural water shortage was highest in February and lowest in September. The simulation results can also facilitate a drought frequency analysis. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T08:36:56Z (GMT). No. of bitstreams: 1 ntu-105-R03h41008-1.pdf: 6010451 bytes, checksum: 93c61558e50f6bf294e95e7acafb5b13 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | Chapter 1 Introduction.............................. 1
1.1 Literature review .............................. 1 1.2 Research objectives and design....................... 2 Chapter 2 Methods .............................. 4 2.1 Study area:The Shihmen Reservoir watershed. . . . . . . . . . . . . . . 4 2.1.1 Reservoir operation of the Shihmen Reservoir . . . . . . . . . . . 5 2.2 Network flow model ............................ 6 2.2.1 Realistic system and priority order . . . . . . . . . . . . . . . . 7 2.2.2 Network formulation........................ 16 2.3 Synthetic flow simulation.......................... 21 2.3.1 Identifying flood flows induced by typhoons . . . . . . . . . . . 23 2.3.2 Analysis of typhoon daily flows .................. 26 2.3.3 Analysisofnon-typhoondailyflows. . . . . . . . . . . . . . . . 28 2.3.4 Generation of synthetic flows ................... 32 2.4 Simulations ................................. 32 Chapter 3 Results.............................. 35 3.1 Assessment of synthetic flows ....................... 35 3.2 Assessment of network flow model..................... 37 3.3 Probability risk assessment......................... 38 Chapter 4 Conclusions and Future Work .............................. 41 4.1 Conclusions................................. 41 4.2 Future work................................. 42 References............................ 43 Appendices............................ 44 A Table of cost coefficients .......................... 44 B Upper bound for the target releases..................... 45 C Contingency table of chi-square goodness-of-fit test.................... 46 | |
dc.language.iso | en | |
dc.title | 以機率觀點看水庫操作之風險評估 | zh_TW |
dc.title | Probability Risk Assessment of Reservoir Operations | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 劉佳明 | |
dc.contributor.oralexamcommittee | 葉小蓁,黃文政 | |
dc.subject.keyword | 水庫操作,流量模擬,網流模式, | zh_TW |
dc.subject.keyword | Reservoir Operations,Flow Simulation,Network Flow Model, | en |
dc.relation.page | 47 | |
dc.identifier.doi | 10.6342/NTU201601881 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2016-08-04 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
顯示於系所單位: | 統計碩士學位學程 |
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