跳到主要內容

臺灣博碩士論文加值系統

(3.236.84.188) 您好!臺灣時間:2021/07/30 03:16
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:石宜軒
研究生(外文):Yi-Hsuan Shih
論文名稱:序率淹水模擬與疏散規劃分析
論文名稱(外文):Stochastic Analysis of Inundation Simulations and Evacuation Planning
指導教授:胡明哲胡明哲引用關係
指導教授(外文):Ming-Che Hu
口試委員:蔡宛珊余化龍溫在弘
口試委員(外文):Wan-Shan TsaiHwa-Lung YuTzai-Hung Wen
口試日期:2015-04-27
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生物環境系統工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:86
中文關鍵詞:不確定性拉丁高次取樣法淹水模擬多目標序率規畫疏散規畫
外文關鍵詞:UncertaintyLatin Hypercube SamplingFlood simulationmultiobjective stochastic programmingevacuation planning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:129
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
洪災為台灣最常見的天然災害之一,防災、減災為政府決策的重要課題。而淹水模擬提供決策者決策依據。在以往淹水模擬,都是使用定率方式,以單一最佳參數進行模擬,並未考慮環境與模式之不確定性,造成結果偏差。序率方法能夠改善不確定性所造成之誤差,而在疏散規畫中,也需將不確定性納入考量,作為決策依序。
序率方式雖能夠將不確定性納入考量,但也因此增加模擬次數,耗時耗力。研究透過4種取樣方法﹝蒙地卡羅法、拉丁高次法、小中取大法、最小相關性法﹞,3個不確定性來源進行河川水位模擬。不確定性來源包含5組上游邊界流量、5組下游邊界水位、17組曼寧糙度係數,共425組參數組合進行模擬。使用425組參數結果平均為參考值。比較不同取樣方法優劣,進而找出建議取樣數量。研究顯示拉丁高次法取樣遠優於蒙地卡羅法,能夠有效降低模式誤差。而小中取大法與最小相關性法雖能夠增加取樣間差異。對於模式結果的改善並不顯著。研究建議取樣數量為參數總組和之35%,可將誤差降低至模式最大誤差之2%。若增加取樣至50%,誤差可再降至1.5%。
本研究亦發展一多目標序率規畫疏散模式。模式分為兩階段:第一階段早期決策,在洪災發生前,決定各避難所容量擴充量;第二階段為洪災反應,在淹水發生後,決定疏散路徑。模式目標為最小擴充成本與平均每人疏散時間。本研究以景美溪與木柵區域為例,透過河川模擬結果所劃定之溢流警戒區域。以需疏散區域作為不確定性來源,使用3種不同情境,做最佳化決策分析。透過多目標規劃能夠表現出疏散路網與避難所間互償關係。研究發現,當疏散成本增加到一定值,疏散路徑不再改變。此時即發生最大疏散量與最小避難所擴充量。研究從水文模式至疏散規畫,都將不確定性納入考量,以作為未來減災規畫設計之依據。


Flood inundation is one of the most usual hazards in Taiwan. To mitigate the impact of flood, inundation mapping plays a significant role. In general, a deterministic approach using optimal parameter sets is applied to analyze the inundation. However, without taking the impact of uncertainties into consideration, it may cause over or underestimate of the model. The stochastic process will improve the weakness of deterministic model. Also, it provides a better basis for decision makers, for example, evacuation planning.
Although stochastic approach considers the influence of uncertainties, it is often a time consuming process. In the study, four sampling strategies (Monte Carlo Simulation, Latin Hypercube Sampling, Maximin Distance, Minimum Correlation), three uncertainty factors are applied to a one dimensional hydraulic model. The uncertainty factors include five water flows as upper boundary condition, five water stages as lower boundary condition, and seventeen manning roughness coefficients. The mean water stage of 425 combination of parameter sets are taken as a reference in comparison of each sampling strategy. Result represents that Latin Hypercube sampling performs almost ten times better than Monte Carlo simulation. And though other sampling strategies can enhance sampling discrepancy, the improvement of the result is not significant. The sample size chosen may depend on the tradeoff between acceptance accuracy of model and computational time. The suggested sample sizes are 35% and 50% of total simulation area.
The study also proposes a multiobjective stochastic programming analysis for uncertain inundation evacuation. A two stage stochastic programming model under inundation uncertainty is built. Expansion of shelter capacity is decided in the first stage before flood. The second stage determines the evacuation plan providing the optimal route to shelters for all evacuees. A case study of MuZha, Taipei is conducted. Based on the result of hydraulic model, three different regions of warning zone for overflow are taken to be the uncertainty resource. The model with multiobjective shows the tradeoff between shelter expansion and transportation time. The result shows that as the unit cost of shelter expansion exceed to a certain level, the total evacuation time and amount of shelter expansion will remain the same. It represents the minimum shelter expansion and maximum evacuation time. From the hydraulic model to optimal programming, the study focuses on how uncertainty affects the models, provides a decision making system for flood inundation.


Abstract I
摘要 III
Content V
List of Figures VIII
List of Tables XII
Chapter 1 Stochastic Inundation Modeling Using Modified Latin Hypercube Sampling Methods 1
Nomenclature 2
1.1 Introduction 5
1.1.1 Preface 5
1.1.2 Purpose 6
1.2 Literature Review 6
1.3 Methodology 8
1.3.1 Uncertainty factors 8
1.3.2 Sampling strategy 9
1.3.3 Model Description 13
1.4 Case Study 23
1.4.1 Cross Section Information 23
1.4.2 Boundary Condition 23
1.4.3 Roughness Coefficient 24
1.5 Result and Discussion 30
1.5.1 Uncertainty of Parameters 30
1.5.2 Monte Carlo Simulation and Latin Hypercube Sampling 31
1.5.3 Comparison of different Latin Hypercube sampling methods 33
1.5.4 Efficiency of Latin Hypercube Sampling 34
1.6 Conclusion and Suggestion 47
1.6.1 Conclusion 47
1.6.2 Suggestion 47
Reference 49
Chapter 2 Multiobjective-Stochastic Programming Analysis of Inundation Evacuation Planning 52
Nomenclature 53
2.1 Introduction 54
2.1.1 Preface 54
2.1.2 Purpose 55
2.2 Literature Review 55
2.3 Methodology 59
2.4 Case Study 65
2.4.1 Network System 65
2.4.2 Shelter Location 65
2.4.3 Stochastic Scenarios 66
2.5 Result and Discussion 73
2.5.1 Case study results 73
2.5.2 Tradeoff between shelter expansion and transportation time 73
2.5.3 Minimal shelter expansion 76
2.6 Conclusion and Suggestion 84
2.6.1 Conclusion 84
2.6.2 Suggestion 84
Reference 86


Apel, H., A. H. Thieken, B. Merz & G. Bloschl (2004) Flood risk assessment and associated uncertainty. Natural Hazards and Earth System Sciences, 4, 295-308.
Aronica, G. T., F. Franza, P. D. Bates & J. C. Neal (2012) Probabilistic evaluation of flood hazard in urban areas using Monte Carlo simulation. Hydrological Process, 26, 3962-3972.
Chen, Y. C. & J. H. Chen (2006) A discussion of uncertainty in ecological risk assessment. Journal of Science and Engineering Technology, 2, 49-60.
Cunge, J. A., F. M. Holly & A. Verwey (1980) Practical Aspects of Computational River Hydraulics. Pitman Publishing Ltd.
Del Castillo, E (2007) Process optimization: a statistical approach. Springer Science & Business Media.
Domeneghetti, A., S. Vorogushyn, A. Castellarin, B. Merz & A. Brath (2013) Probabilistic flood hazard mapping: effects of uncertain boundary conditions. Hydrology and Earth System Sciences, 17, 3127-3140.
Fang, K. T., C. X. Ma & P. Winker (2000) Centered L2-discrepancy of random sampling and Latin hypercube design, and construction of uniform deigns. Mathematics of Computation, 71, 275-296.
Harr, M. E. (1989) Probabilistic estimates for multivariate analyses. Applied Mathematical Modelling, 13, 313-318.
Hsu, H. M (2013) Uncertainty assessment of urban inundation simulations and analysis of probabilistic flooding maps. In Bioenvironmental Systems Engineering. National Taiwan University.
Hung, B. Y., Y. Amemiya & C. F. Wu (2010) Probability-based latin hypercube designs for slid-rectangular regions. Biometrika, 1-9.
Husslage, B., G. Rennen, E. R. van Dam & D. den Hertog (2009) Space-filling Latin hypercube designs for computer experiments.
Janssen, H. (2013) Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence. Reliability Engineering and System Safety, 109, 123-132.
Joseph, V. R. & Y. Hung (2008) Orthogonal-maximin Latin hypercube designs. Statistica Sinica, 18, 171.
Kalyanapu, A. J., D. R. Judi, T. N. McPherson & S. J. Burian (2012) Monte Carlo-based flood modelling framework for estimating probability weighted flood risk. Journal of Flood Risk Management, 5, 37-48.
Kucherenko, S., D. Albrecht & A. Saltelli (2011) Comparison of Latin Hypercube and quasi Monte Carlo sampling techniques.
Lai, Y. W (2009) Investigation on flood inundation probability maps. In Department of Bioenvironmental System Engineering. National Taiwan Universisy.
Lian, Y. & B. C. Yen (2003) Comparison of risk calculation methods for a culvert. Journal of hydraulic engineering, 129, 140-152.
Maskey, S. & V. Guinot (2003) Improved first-order second moment method for uncertainty estimation in flood forecasting. Hydrological sciences journal, 48, 183-196.
McKay, M. D., R. J. Beckman & W. J. Conover (1979) Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21, 239-245.
National Research Council (2000) Risk analysis and uncertainty in flood damage reduction studies. National Academic Press, Washington DC.
Pace, L. A. & C. Salazar-Lazaro (1996). Uniformly distributed sequence and their discrepancies. Oregon State University.
Palisade Corporation (2010) Guide to using @RISK: Risk analysis and simulation add-in for Microsoft® Excel
Pappenberger, F., P. Matgen, K. J. Beven, J. B. Henry, L. Pfister & P. de Fraipont (2006) Influence of uncertain boundary conditions and model structure on flood inundation predictions. Advances in Water Resources, 29, 1430-1449.
Rosenblueth, E. (1975) Point estimates for probability moments. Proceedings of the National Academy of Sciences, 72, 3812-3814.
Rosenblueth, E. (1981) Two-point estimates in probabilities. Applied Mathematical Modelling, 5, 329-335.
Smenoe, C. M., E. J. Nelson, A. K. Zundel & A. W. Miller (2007) Demonstrating floodplain uncertainty using flood probability maps. Journal of American Water Resources Association, 43, 359-371.
Smith, P. J., T. Kojiri & K. Sekii (2006) Risk-based flood evacuation decision using a distributed rainfall-runoff model. Annuals of Disaster Prevention Research Institute, 49, 717-732.
Vongisessomjai, S., T. Tinysanchali & C. Chaiwat. 1985. Bangkok Flood Plain Model. In 21th IAHR Congress, 433-438. Melburn, Australia.
Water Resource Agency (2005) Jingmei River Environment Management Planning.
Water Resource Agency (2009) Planning of river environment rehabilitation in the middle-down reach of Jingmei River.
Yang, J. (2011) Convergence and uncertainty analysis in Monte Carlo based sensitivity analysis. Environmental Modelling and Software, 26, 444-457.
Yu, P.-S., T.-C. Yang & S.-J. Chen (2001) Comparison of uncertainty analysis methods for a distributed rainfall–runoff model. Journal of Hydrology, 244, 43-59.
Bretschneider, S. 2013. Mathematical models for evacuation planning in urban areas. New York: Springer Science+Business Media.
Ford, J. & D. R. Fulkerson (1958) Construcing maximal dynamic flows from static flows. Operations Research, 6, 419-433.
Hamacher, H. W. & S. A. Tjandra (2001) Mathematical modelling of evacuation problems: A state of art. Fraunhofer-Institut für Techno-und Wirtschaftsmathematik, Fraunhofer (ITWM).
Kongsomsaksakul, S., C. Yang & A. CHen (2005) Shelter location-allocation model for flood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies, 6.
Li, C. Y., L. Nozick, N. Xu & R. Davidson (2012) Shelter location and transportation planning under hurriance conditions. Transportation Reasearch Part E, 48, 715-729.
Nash, J. C. (2000) The (Dantzig) simplex method for linear programming. Computing in Science & Engineering, 2, 29-31.
Winston, W. L. (2003) Operations Research: Applications and Algorithms. Cengage Learning.
Yao, T., S. R. Mandala & B. D. Chung (2009) Evacuation transportation planning under uncertainty: a robust optimization approach. 2009, 9, 171-189.
Yeo, G. L. & C. A. Cornell (2009) Post-quake decision analysis using dynamic programming. Earthquake Engineering and Structural Dynamics, 38, 79-93.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top