基于灰色系统理论和云模型的反精确洪水灾害分析
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摘要
随着我国社会经济的迅速发展和城市化进程的不断加快,洪水灾害已经成为制约经济社会可持续发展、威胁人民生命财产安全保障、以及阻碍我国和谐社会发展进程的重要影响因素。根据洪水灾害系统的不确定性,以及系统的高维性、复杂性、开放性以及动态性,从系统科学的观点看,洪水灾害系统是一个动态复杂大系统。毫无疑问,针对这一复杂大系统的控制与管理,无论是采用经典的控制理论,还是采用传统的运筹学技术,都将遇到诸如数据资料不完备、信息不确定性考虑不充分、模型可靠性低等方面的困难。本文基于定性与定量的综合集成方法,把系统科学的理论和方法以及基于灰色理论和云模型等反精确技术引入对不确定性洪水灾害系统的研究中,系统地探讨了洪水灾害的模拟、预测、评估的综合分析方法,旨在建立洪水灾害分析的不确定性系统分析理论、反精确方法与技术体系,实现对复杂环境下洪水灾害不确定性系统的分析、预测、推理及评价,为进一步对洪水灾害的有效调控和管理提供科学的依据,使社会经济朝着协调的方向发展。
具体说来,文章基于灰色理论和云模型的反精确方法,对不确定性洪水灾害系统进行分析研究,包括灾变预测、中长期径流预报、生命损失推理,以及洪灾等级综合评估。其主要研究内容和成果如下：
(1)梳理了洪水灾害系统的复杂性特征和基本研究对象,通过对现实世界中的不确定性原理及其普遍存在性的分析,提出对不确定性洪水灾害系统进行分析的反精确性软计算方法的研究方案与具体实现思路。
(2)由于实际运用中洪水灾害数据少、不确定性因素影响众多,作者根据灰色理论对序列预测所要求的最低信息量,建立洪水灾变点预测的灰色动态模型群,来对洪水灾害进行基于年径流量的参考性灰色灾变预测。以新疆雅马渡水文站的多年径流量数据为例,对基于灰色模型群的洪水灾变预测进行实例研究。
(3)基于对洪水灾害系统的灰色不确定性分析,建立多元时变灰色预测模型,并基于信息熵原理,用熵权法将该模型与非时变的免疫神经网络模型、最小二乘支持向量机模型进行并联组合建模,来做基于年径流量的洪水参考性中长期预报,分析各单项模型各自的优、缺点,以及组合预测建模的实用意义。最后,以新疆伊犁河雅马渡水文站的年径流预测为例,对该站年径流量进行基于信息熵的并联组合预测建模,并与三个单项模型的预测结果进行比较分析,证实了组合预测的合理性、普适性和可靠性。
(4)针对国外计算生命损失经验模型的局限性,具体分析导致生命损失的各种影响因素的不确定性,选取起决定作用的三个因素,即洪水强度、预警时间、人们对洪水危害性意识程度,合理划分构成的15种洪水灾害情景的组合模式,按照国内外经验确定洪水灾害生命损失率推理规则,运用表达信息不确定性的云模型来将其定性概念量化建模,然后对定量的待估样本按该规则做定性的推理,得到定性、定量的综合推理。
(5)为了克服传统白化权函数的局限,将表示信息模糊性和随机性的定性、定量转换的云模型引入灰色白化权函数的表达,对传统白化权函数进行改进,建立基于灰色云模型的白化权函数,用灰色云聚类模型来对洪水灾害损失进行等级评估。并以1989～1990年间我国部分省市发生的45个洪水灾害的灾情案例作为评估实例,对待估样本进行基于变权、定权的灰色云聚类,得到灾情评估结果,证实了该方法的实用性。
With the rapid development of China's economy and the accelerating process of urbanization, the flood disaster has constrained the economic and social sustainable development, threatened the life and property security, and impeded the process of harmonious social development. According to the uncertainty, the hi-dimensional, complex, open and dynamic characteristics of the flood disaster system, from a system science point of view, flood disaster system is a dynamic complex system. There is no doubt that for the control and management of this complex system, whether classical control theory, or traditional operations research techniques, will encounter difficulties. In this article, based on the comprehensive integration of qualitative and quantitative methods, the system science theories and the anti-accuracy technique based on gray theory and cloud model etc. are introduced in the uncertainty study of flood disaster system. In addition, the integrated analysis approach for the simulation, prediction and evaluation of flood disaster has been systematically explored, for the establishment of uncertainty system theory, anti-accuracy method and technology system for flood disaster analysis. This provides the scientific basis for effective control and management of flood disaster in order to harmoniously develop social economics.
Specifically, the article based on the anti-accuracy method of the gray theory and cloud model for analysis of the uncertainty of the flood disaster systems, including disaster prediction, mid- and long-term runoff forecasting, reasoning of life loss, and comprehensive assessment of disaster level. The main research contents and results are as follows:
(1) Sorted out the complexity of the flood disaster system and the basic research objective, and analyzed the uncertainty principle and the widespread nature in the real world. On this basis, the anti-accuracy calculation method for analysis of the uncertainty of flood disasters was proposed.
(2) Established the gray dynamic models to predict flood disasters on the basis of annual runoff. Taking the Yamadu station in Xinjiang as the research area, the flood disaster was predicted.
(3) Based on the gray uncertainty discussion of flood disaster system, a prediction model with time-varying multiple parameters had been set up. Based on the information entropy principle, the entropy-weight was used to parallel combine this model with the non-time-varying immune neural network model and the least-squares support vector machine model for mid- and long-term flood forecasting, analysis of the advantages and disadvantages of each single model and the practical significance of combined predictive modeling. Finally, taking the annual runoff forecasting of Yamadu Station in Xinjiang as a case study, the rationality, universality and reliability of the combined model have been confirmed.
(4) Selected three major factors of life loss in flood disasters, including flood intensity, warning time, people's awareness of flood danger, categorized into 15 flood disaster scenarios of different combination. In accordance with domestic and foreign experience, the inference rule of life loss was determined. The cloud model was used to set up a quantitative model for the qualitative concept, and then with this rule the qualitative reasoning was performed for quantitative samples to be estimated, leading to the comprehensive qualitative and quantitative reasoning results
(5) Refined the traditional whitening-weight function using the cloud model, which is a representation of both fuzziness and stochastic, and could take a bi-directional conversion between qualitative and quantitative. In this way, a grey-cloud clustering model is proposed for the synthetic ranking evaluation of flood disaster. The novel method is applied to evaluate the 45 flood disaster cases of several provinces in 1989～1999, and taking the amount of building collapse, affected area, number of victims, direct economic loss as evaluation indicators. The result shows that this method has much rationality and utility for flood disaster evaluation.
具体说来,文章基于灰色理论和云模型的反精确方法,对不确定性洪水灾害系统进行分析研究,包括灾变预测、中长期径流预报、生命损失推理,以及洪灾等级综合评估。其主要研究内容和成果如下：
(1)梳理了洪水灾害系统的复杂性特征和基本研究对象,通过对现实世界中的不确定性原理及其普遍存在性的分析,提出对不确定性洪水灾害系统进行分析的反精确性软计算方法的研究方案与具体实现思路。
(2)由于实际运用中洪水灾害数据少、不确定性因素影响众多,作者根据灰色理论对序列预测所要求的最低信息量,建立洪水灾变点预测的灰色动态模型群,来对洪水灾害进行基于年径流量的参考性灰色灾变预测。以新疆雅马渡水文站的多年径流量数据为例,对基于灰色模型群的洪水灾变预测进行实例研究。
(3)基于对洪水灾害系统的灰色不确定性分析,建立多元时变灰色预测模型,并基于信息熵原理,用熵权法将该模型与非时变的免疫神经网络模型、最小二乘支持向量机模型进行并联组合建模,来做基于年径流量的洪水参考性中长期预报,分析各单项模型各自的优、缺点,以及组合预测建模的实用意义。最后,以新疆伊犁河雅马渡水文站的年径流预测为例,对该站年径流量进行基于信息熵的并联组合预测建模,并与三个单项模型的预测结果进行比较分析,证实了组合预测的合理性、普适性和可靠性。
(4)针对国外计算生命损失经验模型的局限性,具体分析导致生命损失的各种影响因素的不确定性,选取起决定作用的三个因素,即洪水强度、预警时间、人们对洪水危害性意识程度,合理划分构成的15种洪水灾害情景的组合模式,按照国内外经验确定洪水灾害生命损失率推理规则,运用表达信息不确定性的云模型来将其定性概念量化建模,然后对定量的待估样本按该规则做定性的推理,得到定性、定量的综合推理。
(5)为了克服传统白化权函数的局限,将表示信息模糊性和随机性的定性、定量转换的云模型引入灰色白化权函数的表达,对传统白化权函数进行改进,建立基于灰色云模型的白化权函数,用灰色云聚类模型来对洪水灾害损失进行等级评估。并以1989～1990年间我国部分省市发生的45个洪水灾害的灾情案例作为评估实例,对待估样本进行基于变权、定权的灰色云聚类,得到灾情评估结果,证实了该方法的实用性。
With the rapid development of China's economy and the accelerating process of urbanization, the flood disaster has constrained the economic and social sustainable development, threatened the life and property security, and impeded the process of harmonious social development. According to the uncertainty, the hi-dimensional, complex, open and dynamic characteristics of the flood disaster system, from a system science point of view, flood disaster system is a dynamic complex system. There is no doubt that for the control and management of this complex system, whether classical control theory, or traditional operations research techniques, will encounter difficulties. In this article, based on the comprehensive integration of qualitative and quantitative methods, the system science theories and the anti-accuracy technique based on gray theory and cloud model etc. are introduced in the uncertainty study of flood disaster system. In addition, the integrated analysis approach for the simulation, prediction and evaluation of flood disaster has been systematically explored, for the establishment of uncertainty system theory, anti-accuracy method and technology system for flood disaster analysis. This provides the scientific basis for effective control and management of flood disaster in order to harmoniously develop social economics.
Specifically, the article based on the anti-accuracy method of the gray theory and cloud model for analysis of the uncertainty of the flood disaster systems, including disaster prediction, mid- and long-term runoff forecasting, reasoning of life loss, and comprehensive assessment of disaster level. The main research contents and results are as follows:
(1) Sorted out the complexity of the flood disaster system and the basic research objective, and analyzed the uncertainty principle and the widespread nature in the real world. On this basis, the anti-accuracy calculation method for analysis of the uncertainty of flood disasters was proposed.
(2) Established the gray dynamic models to predict flood disasters on the basis of annual runoff. Taking the Yamadu station in Xinjiang as the research area, the flood disaster was predicted.
(3) Based on the gray uncertainty discussion of flood disaster system, a prediction model with time-varying multiple parameters had been set up. Based on the information entropy principle, the entropy-weight was used to parallel combine this model with the non-time-varying immune neural network model and the least-squares support vector machine model for mid- and long-term flood forecasting, analysis of the advantages and disadvantages of each single model and the practical significance of combined predictive modeling. Finally, taking the annual runoff forecasting of Yamadu Station in Xinjiang as a case study, the rationality, universality and reliability of the combined model have been confirmed.
(4) Selected three major factors of life loss in flood disasters, including flood intensity, warning time, people's awareness of flood danger, categorized into 15 flood disaster scenarios of different combination. In accordance with domestic and foreign experience, the inference rule of life loss was determined. The cloud model was used to set up a quantitative model for the qualitative concept, and then with this rule the qualitative reasoning was performed for quantitative samples to be estimated, leading to the comprehensive qualitative and quantitative reasoning results
(5) Refined the traditional whitening-weight function using the cloud model, which is a representation of both fuzziness and stochastic, and could take a bi-directional conversion between qualitative and quantitative. In this way, a grey-cloud clustering model is proposed for the synthetic ranking evaluation of flood disaster. The novel method is applied to evaluate the 45 flood disaster cases of several provinces in 1989～1999, and taking the amount of building collapse, affected area, number of victims, direct economic loss as evaluation indicators. The result shows that this method has much rationality and utility for flood disaster evaluation.
引文
[1]向立云.洪水灾害特性变化分析.水利发展研究,2002,2(12)
[2]徐娜.上下千年话洪水——评述《中国大洪水——灾害性洪水述要》.中国减灾,2006,(7)
[3]Huang Z, Zhou J, Song L, et al. Flood disaster loss comprehensive evaluation model based on optimization support vector machine. Expert Systems with Applications,2010,37(5):3810～3814
[4]Jiang W, Deng L, Chen L, et al. Risk assessment and validation of flood disaster based on fuzzy mathematics. Progress in Natural Science,2009,19(10):1419～ 1425
[5]Miceli R, Sotgiu I, Settanni M. Disaster preparedness and perception of flood risk: A study in an alpine valley in Italy. Journal of Environmental Psychology,2008, 28(2):164～173
[6]Wei Y, Xu W, Fan Y, et al. Artificial neural network based predictive method for flood disaster. Computers & Industrial Engineering,2002,42(2-4):383～390
[7]Yin H, Li C. Human impact on floods and flood disasters on the Yangtze River. Geomorphology,2001,41(2-3):105～109
[8]Zhang J, Zhou C, Xu K, et al. Flood disaster monitoring and evaluation in China. Global Environmental Change Part B:Environmental Hazards,2002,4(2-3): 33～43
[9]Mustafa D. Reinforcing vulnerability? Disaster relief, recovery, and response to the 2001 flood in Rawalpindi, Pakistan. Global Environmental Change Part B: Environmental Hazards,2003,5(3-4):71～82
[10]Wilhite M J, Haberman M L, Macejkovic C. Responding to disaster:Flood in North Dakota. Journal of the American Psychiatric Nurses Association,1997, 3(5):157～165
[11]Bangladesh:FLOOD DISASTER. The Lancet,1987,330(8559):619～620
[12]Pearce F. China faces quake lakes flood disaster. The New Scientist,2008, 198(2659):8～9
[13]Barata I A, Llovera I, Ward M, et al. Is your household prepared for a disaster
such as fire, flood, earthquake, blackout, or a terrorist attack in your community?. Annals of Emergency Medicine,2004,44(Supplement 1):S107
[14]Li X, Tan H, Li S, et al. Years of potential life lost in residents affected by floods in Hunan, China. Transactions of the Royal Society of Tropical Medicine and Hygiene,2007,101(3):299～304
[15]Moffat R J. Describing the uncertainties in experimental results. Experimental Thermal and Fluid Science,1988,1(1):3～17
[16]Nilsen T, Aven T. Models and model uncertainty in the context of risk analysis. Reliability Engineering & System Safety,2003,79(3):309～317
[17]Moschandreas D J, Karuchit S. Scenario-model-parameter:a new method of cumulative risk uncertainty analysis. Environment International,2002,28(4): 247～261
[18]Xia X, Chen X, Zhang Y, et al. Grey bootstrap method of evaluation of uncertainty in dynamic measurement. Measurement,2008,41(6):687～696
[19]Refsgaard J C, van der Sluijs J P, Hberg A L, et al. Uncertainty in the environmental modelling process-A framework and guidance. Environmental Modelling & Software,2007,22(11):1543～1556
[20]Janak S L, Lin X, Floudas C A. A new robust optimization approach for scheduling under uncertainty:II. Uncertainty with known probability distribution. Computers & Chemical Engineering,2007,31(3):171～195
[21]Refsgaard J C, Nilsson B, Brown J, et al. Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB). Environmental Science & Policy, 2005,8(3):267～277
[22]Brown J D, Heuvelink G B M. The Data Uncertainty Engine (DUE):A software tool for assessing and simulating uncertain environmental variables. Computers& Geosciences,2007,33(2):172～190
[23]Franceschini S, Tsai C W. Assessment of uncertainty sources in water quality modeling in the Niagara River. Advances in Water Resources,2010,33(4):493～ 503
[24]Khadam I M, Kaluarachchi J J. Water quality modeling under hydrologic variability and parameter uncertainty using erosion-scaled export coefficients. Journal of Hydrology,2006,330(1-2):354～367
[25]geles Herrador M, Gonzez A G. Evaluation of measurement uncertainty in
analytical assays by means of Monte-Carlo simulation. Talanta,2004,64(2): 415～422
[26]Hossain F, Anagnostou E N. Assessment of a stochastic interpolation based parameter sampling scheme for efficient uncertainty analyses of hydrologic models. Computers & Geosciences,2005,31(4):497～512
[27]亨利·n·波拉克.不确定的科学与不确定的世界,李萍萍.上海：上海科技教育出版社,2005.
[28]丹·巴鲁奇,麦克斯·维斯.以不确定性应对不确定性.21世纪商业评论,2009,(03)
[29]李金锋,黄崇福,宗恬,反精确现象与形式化研究.系统工程理论与实践,2005,25(4)
[30]Chongfu H. An anti-accuracy rule rooting in information diffusion techniques. 2004
[31]Rafik A A. Decision making theory with imprecise probabilities.2009
[32]Kolmogorov A. Logical basis for information theory and probability theory. Information Theory, IEEE Transactions on,1968,14(5):662～664
[33]L. A Z. Possibility theory vs. probability theory in decision analysis.1977
[34]Riecan B. Probability theory and the operations with IF-sets.2008
[35]Yamada K. Probability-possibility transformation based on evidence theory.2001
[36]Halliwell J, Qiang S. Towards a linguistic probability theory.2002
[37]Hossain F, Anagnostou E N, Dinku T. Sensitivity analyses of satellite rainfall retrieval and sampling error on flood prediction uncertainty. Geoscience and Remote Sensing, IEEE Transactions on,2004,42(1):130～139
[38]L. A Z. Pruf and its application to inference from fuzzy propositions.1977
[39]邓聚龙.灰色控制系统.华中工学院学报,1982,(03)：9～18
[40]王光远.未确知信息及其数学处理.哈尔滨建筑工程学院学报,1990,(04)：1～9
[41]李德毅,孟海军,史雪梅.隶属云和隶属云发生器.计算机研究与发展,1995,(06)
[42]李德毅.知识表示中的不确定性.中国工程科学,2000,(10)：73～79
[43]Ebanks B, Karwowski W, Ostaszewski K. Application of measures of fuzziness to risk classification in insurance.1992
[44]Yoshida Y. Mean Values of Fuzzy Numbers and the Measurement of Fuzziness
by Evaluation Measures.2006
[45]De Baets B, Kerre E E, Nasuto S. Measuring fuzziness:An evaluation problem. 1993
[46]Pal N R, Bezdek J G. Several new classes of measures of fuzziness.1993
[47]Takayanagi S, Cliff N. The fuzziness index for examining human statistical decision-making.1993
[48]Ayyub B M. Uncertainty modelling and analysis:College Park, Maryland, USA: 1990
[49]Viertl R, Hule H. On Bayes'theorem for fuzzy data:Statistical papers:1991
[50]陈守煜.水利水文水资源与环境模糊集分析大连：大连工学院出版社,1987.
[51]杨叔子.时间序列分析的工程应用武汉：华中理工大学出版社,1991.
[52]冯宛平.利用月平均量构造郑州地区月降水量预测：全国第一届灰色系统与农业学术会议：1984
[53]袁嘉祖.灰色系统理论及其应用北京：科学出版社,1991.
[54]夏军.水文水资源管理中的统计理论及不确定性的研究与展望.水资源研究,1992,13(1)
[55]冯德益.模糊地震学北京：地震出版社,1992.
[56]刘光吉.模糊灰色马氏过程在水库洪水预报中的应用.水电能源科学,1989,7(2)
[57]Si-Feng L, Forrest J. Advances in grey systems theory and its applications.2007
[58]Mian-Yun C. Uncertainty analysis and grey modeling.1990
[59]Liu Z, Wang Q, Li Q. Universal information of uncertainty systematic theory. 2007
[60]郭淳,李祚泳,党媛.基于免疫进化算法的BP网络模型在径流预测中的应用.水资源保护,2009,25(5)
[61]赵红标,吴义斌.基于支持向量机的中长期入库径流预报.黑龙江水专学报,2009,36(3)
[62]方思和.浅谈相应水位法对洪水预报中的应用.广西水利水电,2009,(1)
[63]段秋生.合成流量法对北洛河刘家河水文站进行洪水预报的探讨.延安大学学报(自然科学版),2005,24(3)
[64]任继周.利用合成流量法预报山区河道洪水有关问题的探讨.北京：2003
[65]陈金庆.马斯京根流量演算法在洪水预报中的应用.中国农村水利水电,2005,(5)
[66]吕世文,冯明一.马斯京根法在郑家屯洪水预报方案中的应用.吉林水利,2004,(12)
[67]巨兴顺,刘波,拜存有.马斯京根分段演算与水箱模型组合在区间洪水过程预报中的应用.水资源与水工程学报,2006,17(3)
[68]Gill M A. Flood routing by the Muskingum method. Journal of Hydrology,1978, 36(3-4):353～363
[69]Guang-Te W, Singh V P. Muskingum method with variable parameters for flood routing in channels. Journal of Hydrology,1992,134(1-4):57～76
[70]Akram Gill M, Mustafa S. On the Muskingum method of flood routing. Advances in Water Resources,1979,2:51～53
[71]Singh V P, Mccann R C. Some notes on Muskingum method of flood routing. Journal of Hydrology,1980,48(3-4):343～361
[72]黄小兰,李麒,Xiao-Lan H, et al.集合卡尔曼滤波在流域水文模型流量预报中的应用.成都信息工程学院学报,2009,24(4)
[73]王船海,吴晓玲,周全.卡尔曼滤波校正技术在水动力学模型实时洪水预报中的应用.河海大学学报(自然科学版),2008,36(3)
[74]陆波.流域水文模型与卡尔曼滤波耦合实时洪水预报研究：[河海大学,2006
[75]Schreider S Y, Young P C, Jakeman A J. An application of the Kalman filtering technique for Streamflow forecasting in the Upper Murray Basin. Mathematical and Computer Modelling,33(6-7):733～743
[76]Neal J C, Atkinson P M, Hutton C W. Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model. Journal of Hydrology,2009,375(3-4):589～600
[77]Clark M P, Rupp D E, Woods R A, et al. Hydrological data assimilation with the ensemble Kalman filter:Use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources,2008,31(10): 1309～1324
[78]Husain T. Kalman filter estimation model in flood forecasting. Advances in Water Resources,1985,8(1):15～21
[79]Wu X, Wang C, Chen X, et al. Kalman filtering correction in real-time forecasting with hydrodynamic model. Journal of Hydrodynamics, Ser. B,2008,20(3):391～ 397
[80]Wang G T, Yao C, Okoren C, et al.4-Point FDF of Muskingum method based on
the complete St Venant equations. Journal of Hydrology,2006,324(1-4):339～ 349
[81]Romanowicz R J, Dooge J C I, Kundzewicz Z W. Moments and cumulants of linearized St. Venant equation. Advances in Water Resources,1988,11(2):92～ 100
[82]张弛,周惠成,李伟.基于模糊推理和关联规则分析的河道洪水预报模型.大连理工大学学报,2008,48(2)
[83]李致家,韩从尚,翁明华.卡尔曼半自适应模型在复杂河道实时洪水预报和行蓄洪调度中的应用.河海大学学报(自然科学版),2001,29(2)
[84]宋绪美.考虑人类活动影响及河道过流能力不确定性的洪水预报及调度研究：[大连理工大学,2008
[85]章四龙.可靠性理论在河道洪水预报中的应用.北京：1997
[86]覃光华,丁晶,刘国东.自适应BP算法及其在河道洪水预报上的应用.水科学进展,2002,13(1)
[87]张金海,王卫民,赵连锋.傅里叶有限差分法三维波动方程正演模拟.地球物理学报,2007,50(6)
[88]沈国光,叶春生.应用水波理论说明含沙洪水过程的异常现象.郑州：2005
[89]郑颖人,赵尚毅,孔位学.极限分析有限元法讲座--Ⅰ岩土工程极限分析有限元法.岩土力学,2005,26(1)
[90]郑宏,李春光,李焯芬.求解安全系数的有限元法.岩土工程学报,2002,24(5)
[91]包为民,张小琴,瞿思敏.感潮河段双向波水位演算模型验证.水科学进展,2009,20(6)
[92]杨静,黄平,钟义龙.北江下游流域洪水预报模型.中山大学学报(自然科学版),2002,41(2)
[93]黄国如,胡和平.第三类边界条件的扩散波洪水演算研究.清华大学学报(自然科学版),2001,41(8)
[94]凌斌.桑园水库洪水管理调度决策支持系统—系统设计与模型研究：[华中科技大学,2005
[95]何新林,刘东旭,郭生练.新疆玛纳斯河流域洪水预报研究.冰川冻土,2004,26(2)
[96]任政,郑旭荣,刘坤.新疆玛纳斯河年径流预报研究.水土保持研究,2005,
12(5)
[97]李存军,邓红霞,朱兵.BP神经网络预测日径流序列的数据适应性分析.四川大学学报(工程科学版),2007,39(2)
[98]李红霞,许士国,范垂仁.基于贝叶斯正则化神经网络的径流长期预报.大连理工大学学报,2006,46(z1)
[99]殷峻暹,陈守煜,邱菊.基于遗传与BP混合算法神经网络预测模型及应用.大连理工大学学报,2002,42(5)
[100]吕佳良,张振刚.基于灰色关联指标筛选的BP神经网络中长期电力负荷滚动预测马尔可夫残差修正模型研究.华东电力,2008,36(9)
[101]严修红,许伦辉.基于神经网络实现的改进灰色组合预测及应用.交通与计算机,2006,24(6)
[102]陈继光,李瑞平.基于网络基坑沉降预测的马尔可夫修正模型.公路交通科技,2005,22(4)
[103]曹启辉,王文圣,汤成友.一种新的小波网络组合预测模型.人民长江,2006,37(11)
[104]Yong-Chao L, Hui-Jun J, Er-Si K, et al. Trend and Characteristics of Variation on the Runoff of the Upper Yellow River above Tangnag.中国沙漠,2000,20(3)
[105]赵昕,宋维玲,何广顺.海洋灾害直接经济损失预测——基于灰色-周期外延组合模型.海洋开发与管理,2007,24(6)
[106]任峰,李伟,丁超.基于灰色-周期外延组合模型的电力负荷预测.电网技术,2007,31(24)
[107]耿德祥,孙惠合,汪顺勤.灰色线性回归在冬小麦产量长期预测中的应用.安徽农业科学,2009,37(24)
[108]李洪波,帅斌.灰色-线性回归组合模型在预测中的应用.陕西工学院学报(自然科学版),2003,19(4)
[109]张欣莉,丁晶.参数投影寻踪回归及其在年径流预测中的应用.四川大学学报(工程科学版),2000,32(3)
[110]杨永生,何平.投影寻踪回归与BP神经网络方法在前汛期降水预测中的比较研究.气象与环境学报,2008,24(1)
[111]张欣莉,丁晶,郑祖国.投影寻踪回归在紫坪埔洪水预报中的应用.四川大学学报(工程科学版),2000,32(2)
[112]Li Y, Chen Y. Grey system theory based parallel combination forecast method and its application. Nanjing, China:IEEE Computer Society,2009
[113]杨丽娟.信息熵在农业科技研究中的应用.安徽农业科学,2009,(35)：17781～17783
[114]王健.论管理科学中的“软科学方法”——系统管理的方法论：The 13th Annual Conference of System Engineering Society of China.2004
[115]Brown C A, Graham W J. Assessing the threat to life from dam failure. Water Resources Bulletin,1988,24(6):1303-1309
[116]Graham W J. A procedure for estimating loss of life caused by dam failure. Bureau of Reclamation Report DSO-99-06,1999,
[117]Dekay M L, Gary H M. Predicting loss of life in cases of dam failure and flash flood. Risk Analysis,1993,13(2):193～205
[118]李雷.大坝风险评价与风险管理北京：中国水利水电出版社,2006.
[119]何金平,廖文来,施玉群.基于可拓学的大坝安全综合评价方法.武汉大学学报(工学版),2008,41(2)
[120]孙玮玮,李雷.基于物元法的大坝风险后果综合评价模型.安全与环境学报,2009,09(2)
[121]孙秀玲,周玉香,曹升乐.水灾灾度定量综合评价模型及应用.山东大学学报(工学版),2006,36(6)
[122]蒋卫国,李京,李忠武.洪水灾害人口风险模糊评价.湖南大学学报(自然科学版),2008,35(9)
[123]张弛,宋绪美,李伟.可变模糊评价法在洪涝灾情评价中的应用.自然灾害学报,2008,17(5)
[124]闻珺,方国华,方正杰.BP神经网络在洪水灾害灾情等级评价中的应用.水利科技与经济,2007,13(1)
[125]闫滨,高真伟,李东艳.RBF神经网络在大坝安全综合评价中的应用.岩石力学与工程学报,2008,27(z2)
[126]张坤,丁新新,洪伟.基于改进投影寻踪法的农业气象灾情综合评价.中国农业气象,2009,30(1)
[127]杨晓华,杨志峰,沈珍瑶,et al.基于投影寻踪的洪水灾情评价插值模型.灾害学,2004,19(4)
[128]刘思峰,朱永达.区域经济评估指标三角隶属函数评估模型.农业工程学报,1993,9(2)：8～13
[129]王洪利,冯玉强.基于灰云的改进白化模型及其在灰色决策中应用.黑龙江大学自然科学学报,2006,23(6)
[130]王洪利.灰云逻辑及其智能决策支持系统.计算机工程与应用,2008,44(13)
[131]王健,肖文杰,盛文.一种改进灰云模型的效能评估方法.微计算机信息,2009,25(12)
[132]李祚泳,邓新民.自然灾害的物元分析情评估模型初探.自然灾害学报,1994,3(2)：28～33
[2]徐娜.上下千年话洪水——评述《中国大洪水——灾害性洪水述要》.中国减灾,2006,(7)
[3]Huang Z, Zhou J, Song L, et al. Flood disaster loss comprehensive evaluation model based on optimization support vector machine. Expert Systems with Applications,2010,37(5):3810～3814
[4]Jiang W, Deng L, Chen L, et al. Risk assessment and validation of flood disaster based on fuzzy mathematics. Progress in Natural Science,2009,19(10):1419～ 1425
[5]Miceli R, Sotgiu I, Settanni M. Disaster preparedness and perception of flood risk: A study in an alpine valley in Italy. Journal of Environmental Psychology,2008, 28(2):164～173
[6]Wei Y, Xu W, Fan Y, et al. Artificial neural network based predictive method for flood disaster. Computers & Industrial Engineering,2002,42(2-4):383～390
[7]Yin H, Li C. Human impact on floods and flood disasters on the Yangtze River. Geomorphology,2001,41(2-3):105～109
[8]Zhang J, Zhou C, Xu K, et al. Flood disaster monitoring and evaluation in China. Global Environmental Change Part B:Environmental Hazards,2002,4(2-3): 33～43
[9]Mustafa D. Reinforcing vulnerability? Disaster relief, recovery, and response to the 2001 flood in Rawalpindi, Pakistan. Global Environmental Change Part B: Environmental Hazards,2003,5(3-4):71～82
[10]Wilhite M J, Haberman M L, Macejkovic C. Responding to disaster:Flood in North Dakota. Journal of the American Psychiatric Nurses Association,1997, 3(5):157～165
[11]Bangladesh:FLOOD DISASTER. The Lancet,1987,330(8559):619～620
[12]Pearce F. China faces quake lakes flood disaster. The New Scientist,2008, 198(2659):8～9
[13]Barata I A, Llovera I, Ward M, et al. Is your household prepared for a disaster
such as fire, flood, earthquake, blackout, or a terrorist attack in your community?. Annals of Emergency Medicine,2004,44(Supplement 1):S107
[14]Li X, Tan H, Li S, et al. Years of potential life lost in residents affected by floods in Hunan, China. Transactions of the Royal Society of Tropical Medicine and Hygiene,2007,101(3):299～304
[15]Moffat R J. Describing the uncertainties in experimental results. Experimental Thermal and Fluid Science,1988,1(1):3～17
[16]Nilsen T, Aven T. Models and model uncertainty in the context of risk analysis. Reliability Engineering & System Safety,2003,79(3):309～317
[17]Moschandreas D J, Karuchit S. Scenario-model-parameter:a new method of cumulative risk uncertainty analysis. Environment International,2002,28(4): 247～261
[18]Xia X, Chen X, Zhang Y, et al. Grey bootstrap method of evaluation of uncertainty in dynamic measurement. Measurement,2008,41(6):687～696
[19]Refsgaard J C, van der Sluijs J P, Hberg A L, et al. Uncertainty in the environmental modelling process-A framework and guidance. Environmental Modelling & Software,2007,22(11):1543～1556
[20]Janak S L, Lin X, Floudas C A. A new robust optimization approach for scheduling under uncertainty:II. Uncertainty with known probability distribution. Computers & Chemical Engineering,2007,31(3):171～195
[21]Refsgaard J C, Nilsson B, Brown J, et al. Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB). Environmental Science & Policy, 2005,8(3):267～277
[22]Brown J D, Heuvelink G B M. The Data Uncertainty Engine (DUE):A software tool for assessing and simulating uncertain environmental variables. Computers& Geosciences,2007,33(2):172～190
[23]Franceschini S, Tsai C W. Assessment of uncertainty sources in water quality modeling in the Niagara River. Advances in Water Resources,2010,33(4):493～ 503
[24]Khadam I M, Kaluarachchi J J. Water quality modeling under hydrologic variability and parameter uncertainty using erosion-scaled export coefficients. Journal of Hydrology,2006,330(1-2):354～367
[25]geles Herrador M, Gonzez A G. Evaluation of measurement uncertainty in
analytical assays by means of Monte-Carlo simulation. Talanta,2004,64(2): 415～422
[26]Hossain F, Anagnostou E N. Assessment of a stochastic interpolation based parameter sampling scheme for efficient uncertainty analyses of hydrologic models. Computers & Geosciences,2005,31(4):497～512
[27]亨利·n·波拉克.不确定的科学与不确定的世界,李萍萍.上海：上海科技教育出版社,2005.
[28]丹·巴鲁奇,麦克斯·维斯.以不确定性应对不确定性.21世纪商业评论,2009,(03)
[29]李金锋,黄崇福,宗恬,反精确现象与形式化研究.系统工程理论与实践,2005,25(4)
[30]Chongfu H. An anti-accuracy rule rooting in information diffusion techniques. 2004
[31]Rafik A A. Decision making theory with imprecise probabilities.2009
[32]Kolmogorov A. Logical basis for information theory and probability theory. Information Theory, IEEE Transactions on,1968,14(5):662～664
[33]L. A Z. Possibility theory vs. probability theory in decision analysis.1977
[34]Riecan B. Probability theory and the operations with IF-sets.2008
[35]Yamada K. Probability-possibility transformation based on evidence theory.2001
[36]Halliwell J, Qiang S. Towards a linguistic probability theory.2002
[37]Hossain F, Anagnostou E N, Dinku T. Sensitivity analyses of satellite rainfall retrieval and sampling error on flood prediction uncertainty. Geoscience and Remote Sensing, IEEE Transactions on,2004,42(1):130～139
[38]L. A Z. Pruf and its application to inference from fuzzy propositions.1977
[39]邓聚龙.灰色控制系统.华中工学院学报,1982,(03)：9～18
[40]王光远.未确知信息及其数学处理.哈尔滨建筑工程学院学报,1990,(04)：1～9
[41]李德毅,孟海军,史雪梅.隶属云和隶属云发生器.计算机研究与发展,1995,(06)
[42]李德毅.知识表示中的不确定性.中国工程科学,2000,(10)：73～79
[43]Ebanks B, Karwowski W, Ostaszewski K. Application of measures of fuzziness to risk classification in insurance.1992
[44]Yoshida Y. Mean Values of Fuzzy Numbers and the Measurement of Fuzziness
by Evaluation Measures.2006
[45]De Baets B, Kerre E E, Nasuto S. Measuring fuzziness:An evaluation problem. 1993
[46]Pal N R, Bezdek J G. Several new classes of measures of fuzziness.1993
[47]Takayanagi S, Cliff N. The fuzziness index for examining human statistical decision-making.1993
[48]Ayyub B M. Uncertainty modelling and analysis:College Park, Maryland, USA: 1990
[49]Viertl R, Hule H. On Bayes'theorem for fuzzy data:Statistical papers:1991
[50]陈守煜.水利水文水资源与环境模糊集分析大连：大连工学院出版社,1987.
[51]杨叔子.时间序列分析的工程应用武汉：华中理工大学出版社,1991.
[52]冯宛平.利用月平均量构造郑州地区月降水量预测：全国第一届灰色系统与农业学术会议：1984
[53]袁嘉祖.灰色系统理论及其应用北京：科学出版社,1991.
[54]夏军.水文水资源管理中的统计理论及不确定性的研究与展望.水资源研究,1992,13(1)
[55]冯德益.模糊地震学北京：地震出版社,1992.
[56]刘光吉.模糊灰色马氏过程在水库洪水预报中的应用.水电能源科学,1989,7(2)
[57]Si-Feng L, Forrest J. Advances in grey systems theory and its applications.2007
[58]Mian-Yun C. Uncertainty analysis and grey modeling.1990
[59]Liu Z, Wang Q, Li Q. Universal information of uncertainty systematic theory. 2007
[60]郭淳,李祚泳,党媛.基于免疫进化算法的BP网络模型在径流预测中的应用.水资源保护,2009,25(5)
[61]赵红标,吴义斌.基于支持向量机的中长期入库径流预报.黑龙江水专学报,2009,36(3)
[62]方思和.浅谈相应水位法对洪水预报中的应用.广西水利水电,2009,(1)
[63]段秋生.合成流量法对北洛河刘家河水文站进行洪水预报的探讨.延安大学学报(自然科学版),2005,24(3)
[64]任继周.利用合成流量法预报山区河道洪水有关问题的探讨.北京：2003
[65]陈金庆.马斯京根流量演算法在洪水预报中的应用.中国农村水利水电,2005,(5)
[66]吕世文,冯明一.马斯京根法在郑家屯洪水预报方案中的应用.吉林水利,2004,(12)
[67]巨兴顺,刘波,拜存有.马斯京根分段演算与水箱模型组合在区间洪水过程预报中的应用.水资源与水工程学报,2006,17(3)
[68]Gill M A. Flood routing by the Muskingum method. Journal of Hydrology,1978, 36(3-4):353～363
[69]Guang-Te W, Singh V P. Muskingum method with variable parameters for flood routing in channels. Journal of Hydrology,1992,134(1-4):57～76
[70]Akram Gill M, Mustafa S. On the Muskingum method of flood routing. Advances in Water Resources,1979,2:51～53
[71]Singh V P, Mccann R C. Some notes on Muskingum method of flood routing. Journal of Hydrology,1980,48(3-4):343～361
[72]黄小兰,李麒,Xiao-Lan H, et al.集合卡尔曼滤波在流域水文模型流量预报中的应用.成都信息工程学院学报,2009,24(4)
[73]王船海,吴晓玲,周全.卡尔曼滤波校正技术在水动力学模型实时洪水预报中的应用.河海大学学报(自然科学版),2008,36(3)
[74]陆波.流域水文模型与卡尔曼滤波耦合实时洪水预报研究：[河海大学,2006
[75]Schreider S Y, Young P C, Jakeman A J. An application of the Kalman filtering technique for Streamflow forecasting in the Upper Murray Basin. Mathematical and Computer Modelling,33(6-7):733～743
[76]Neal J C, Atkinson P M, Hutton C W. Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model. Journal of Hydrology,2009,375(3-4):589～600
[77]Clark M P, Rupp D E, Woods R A, et al. Hydrological data assimilation with the ensemble Kalman filter:Use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources,2008,31(10): 1309～1324
[78]Husain T. Kalman filter estimation model in flood forecasting. Advances in Water Resources,1985,8(1):15～21
[79]Wu X, Wang C, Chen X, et al. Kalman filtering correction in real-time forecasting with hydrodynamic model. Journal of Hydrodynamics, Ser. B,2008,20(3):391～ 397
[80]Wang G T, Yao C, Okoren C, et al.4-Point FDF of Muskingum method based on
the complete St Venant equations. Journal of Hydrology,2006,324(1-4):339～ 349
[81]Romanowicz R J, Dooge J C I, Kundzewicz Z W. Moments and cumulants of linearized St. Venant equation. Advances in Water Resources,1988,11(2):92～ 100
[82]张弛,周惠成,李伟.基于模糊推理和关联规则分析的河道洪水预报模型.大连理工大学学报,2008,48(2)
[83]李致家,韩从尚,翁明华.卡尔曼半自适应模型在复杂河道实时洪水预报和行蓄洪调度中的应用.河海大学学报(自然科学版),2001,29(2)
[84]宋绪美.考虑人类活动影响及河道过流能力不确定性的洪水预报及调度研究：[大连理工大学,2008
[85]章四龙.可靠性理论在河道洪水预报中的应用.北京：1997
[86]覃光华,丁晶,刘国东.自适应BP算法及其在河道洪水预报上的应用.水科学进展,2002,13(1)
[87]张金海,王卫民,赵连锋.傅里叶有限差分法三维波动方程正演模拟.地球物理学报,2007,50(6)
[88]沈国光,叶春生.应用水波理论说明含沙洪水过程的异常现象.郑州：2005
[89]郑颖人,赵尚毅,孔位学.极限分析有限元法讲座--Ⅰ岩土工程极限分析有限元法.岩土力学,2005,26(1)
[90]郑宏,李春光,李焯芬.求解安全系数的有限元法.岩土工程学报,2002,24(5)
[91]包为民,张小琴,瞿思敏.感潮河段双向波水位演算模型验证.水科学进展,2009,20(6)
[92]杨静,黄平,钟义龙.北江下游流域洪水预报模型.中山大学学报(自然科学版),2002,41(2)
[93]黄国如,胡和平.第三类边界条件的扩散波洪水演算研究.清华大学学报(自然科学版),2001,41(8)
[94]凌斌.桑园水库洪水管理调度决策支持系统—系统设计与模型研究：[华中科技大学,2005
[95]何新林,刘东旭,郭生练.新疆玛纳斯河流域洪水预报研究.冰川冻土,2004,26(2)
[96]任政,郑旭荣,刘坤.新疆玛纳斯河年径流预报研究.水土保持研究,2005,
12(5)
[97]李存军,邓红霞,朱兵.BP神经网络预测日径流序列的数据适应性分析.四川大学学报(工程科学版),2007,39(2)
[98]李红霞,许士国,范垂仁.基于贝叶斯正则化神经网络的径流长期预报.大连理工大学学报,2006,46(z1)
[99]殷峻暹,陈守煜,邱菊.基于遗传与BP混合算法神经网络预测模型及应用.大连理工大学学报,2002,42(5)
[100]吕佳良,张振刚.基于灰色关联指标筛选的BP神经网络中长期电力负荷滚动预测马尔可夫残差修正模型研究.华东电力,2008,36(9)
[101]严修红,许伦辉.基于神经网络实现的改进灰色组合预测及应用.交通与计算机,2006,24(6)
[102]陈继光,李瑞平.基于网络基坑沉降预测的马尔可夫修正模型.公路交通科技,2005,22(4)
[103]曹启辉,王文圣,汤成友.一种新的小波网络组合预测模型.人民长江,2006,37(11)
[104]Yong-Chao L, Hui-Jun J, Er-Si K, et al. Trend and Characteristics of Variation on the Runoff of the Upper Yellow River above Tangnag.中国沙漠,2000,20(3)
[105]赵昕,宋维玲,何广顺.海洋灾害直接经济损失预测——基于灰色-周期外延组合模型.海洋开发与管理,2007,24(6)
[106]任峰,李伟,丁超.基于灰色-周期外延组合模型的电力负荷预测.电网技术,2007,31(24)
[107]耿德祥,孙惠合,汪顺勤.灰色线性回归在冬小麦产量长期预测中的应用.安徽农业科学,2009,37(24)
[108]李洪波,帅斌.灰色-线性回归组合模型在预测中的应用.陕西工学院学报(自然科学版),2003,19(4)
[109]张欣莉,丁晶.参数投影寻踪回归及其在年径流预测中的应用.四川大学学报(工程科学版),2000,32(3)
[110]杨永生,何平.投影寻踪回归与BP神经网络方法在前汛期降水预测中的比较研究.气象与环境学报,2008,24(1)
[111]张欣莉,丁晶,郑祖国.投影寻踪回归在紫坪埔洪水预报中的应用.四川大学学报(工程科学版),2000,32(2)
[112]Li Y, Chen Y. Grey system theory based parallel combination forecast method and its application. Nanjing, China:IEEE Computer Society,2009
[113]杨丽娟.信息熵在农业科技研究中的应用.安徽农业科学,2009,(35)：17781～17783
[114]王健.论管理科学中的“软科学方法”——系统管理的方法论：The 13th Annual Conference of System Engineering Society of China.2004
[115]Brown C A, Graham W J. Assessing the threat to life from dam failure. Water Resources Bulletin,1988,24(6):1303-1309
[116]Graham W J. A procedure for estimating loss of life caused by dam failure. Bureau of Reclamation Report DSO-99-06,1999,
[117]Dekay M L, Gary H M. Predicting loss of life in cases of dam failure and flash flood. Risk Analysis,1993,13(2):193～205
[118]李雷.大坝风险评价与风险管理北京：中国水利水电出版社,2006.
[119]何金平,廖文来,施玉群.基于可拓学的大坝安全综合评价方法.武汉大学学报(工学版),2008,41(2)
[120]孙玮玮,李雷.基于物元法的大坝风险后果综合评价模型.安全与环境学报,2009,09(2)
[121]孙秀玲,周玉香,曹升乐.水灾灾度定量综合评价模型及应用.山东大学学报(工学版),2006,36(6)
[122]蒋卫国,李京,李忠武.洪水灾害人口风险模糊评价.湖南大学学报(自然科学版),2008,35(9)
[123]张弛,宋绪美,李伟.可变模糊评价法在洪涝灾情评价中的应用.自然灾害学报,2008,17(5)
[124]闻珺,方国华,方正杰.BP神经网络在洪水灾害灾情等级评价中的应用.水利科技与经济,2007,13(1)
[125]闫滨,高真伟,李东艳.RBF神经网络在大坝安全综合评价中的应用.岩石力学与工程学报,2008,27(z2)
[126]张坤,丁新新,洪伟.基于改进投影寻踪法的农业气象灾情综合评价.中国农业气象,2009,30(1)
[127]杨晓华,杨志峰,沈珍瑶,et al.基于投影寻踪的洪水灾情评价插值模型.灾害学,2004,19(4)
[128]刘思峰,朱永达.区域经济评估指标三角隶属函数评估模型.农业工程学报,1993,9(2)：8～13
[129]王洪利,冯玉强.基于灰云的改进白化模型及其在灰色决策中应用.黑龙江大学自然科学学报,2006,23(6)
[130]王洪利.灰云逻辑及其智能决策支持系统.计算机工程与应用,2008,44(13)
[131]王健,肖文杰,盛文.一种改进灰云模型的效能评估方法.微计算机信息,2009,25(12)
[132]李祚泳,邓新民.自然灾害的物元分析情评估模型初探.自然灾害学报,1994,3(2)：28～33
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