基于智能计算的预测模型研究及其在公共危机管理中的应用
|
摘要
预测问题是一个被广泛关注的研究课题,在各个领域都有众多学者提出各种各样的预测模型来解决现实世界的实际问题。本研究中的预测问题可以分为两类,第一类是时间序列预测,第二类是结合地理信息系统(GIS)的时空分析。
时间序列预测模型主要分为线性模型和非线性模型,线性模型代表的方法有ARMA、ARIMA模型,这些模型能够根据数据进行动态调整,实现预测精度的提高,但是它们无法对数据波动的各个因素进行全面分析,因而预测精度会受到影响。非线性模型的典型代表是人工神经网络模型,它具有很强的并行计算能力,且能够以任意精度逼近任何非线性连续函数,因此其在预测领域得到了广泛的应用,但是其容易出现过拟合现象和陷入局部最小值。混沌粒子群算法(Chaos Particle Swarm Optimization,简称CPSO)是一种基于群体智能的进化计算技术并融合了混沌思想,利用群体中的个体对信息的共享使整个群体的运动在问题求解空间中产生从无序到有序的演化过程,从而获得最优解,群体智能技术被广泛地应用到优化领域,其可以有效地寻找全局最优解。因此,本研究以上述两种智能计算方法为基础,提出了以混沌粒子群优化BP神经网络的混合预测模型(CPSO-BP),并从以下两方面深入研究提高模型的预测精度：1)混合模型的参数选择。通过大量实验深入剖析了智能寻优算法与神经网络结合的混合算法的性能,用实验证实了如果模型参数选择不合适,混合模型的预测能力未必比单一预测模型要好,因此在CPSO-BP模型的基础上提出了两种输入样本数据选择方式及预测模型输入参数的确定方法,从而使预测模型达到最优状态。2)样本数据的处理。在前面研究基础上,从样本数据的处理入手,将小波变换的思想融入混合预测模型,进一步发挥模型的预测能力并保证模型的稳定性。
在时空分析预测方面,本研究提出了投影的思想：其核心思想就是将复杂的问题简单化,实现由N维空间向多个一维空间的映射,既降低了计算难度,又降低计算时间,进而降低算法的时间复杂度。本文在此基础上提出了基于投影思想的层次聚类热点分析模型,并从理论层面深入讨论了算法的时空复杂度,该模型既包含了RNNH预测准确的特点,同时在时间复杂度上优于K-means算法。
本研究以公共危机事件预测模型构建为研究对象,已建立面向服务架构的公共危机管理案例知识库系统平台为目标,从理论和实践两方面实现对公共危机管理预测模型的研究。
基于上述研究背景,本研究建立了三个模型应用场景：1)考虑到地震发生的不确定性、H1N1爆发地方的分散特性以及犯罪地点发生的不确定性,本研究将投影思想和层次聚类思想相融合,提出了基于投影的热点分析预测模型,并将该预测模型成功应用到奥克兰犯罪分析、美国地震数据分析、H1Nl发生热点分析以及全球地震数据分析中。2)以能源危机为研究背景切入点,本研究将上述提到的基于参数选择的混沌PSO优化BP神经网络模型应用到风速预测中,通过实验结果分析,验证了预测模型的有效性。3)考虑流感数据预测的重要性,本研究将基于小波分析的混沌PSO优化BP神经网络模型应用到美国、加拿大、澳大利亚和南非四个国家的流感预测中,并从预测准确度和算法稳定性上进行了说明。
本研究的主要成果及贡献如下所示：
1)发展了集合论在预测领域的应用,并将其作为基础分析工具成功应用到基于SOA的公共危机管理案例知识库系统平台中。
2)在时空分析领域中,提出基于投影思想的层次热点分析预测模型,并将此模型成功应用到传染病预测、犯罪分析和地震热点分析中。
3)引入两种新的基于智能计算的模型,并将这两个模型成功应用到风速预测和流感预测中,并对模型的有效性进行了验证。
4)构建了基于SOA的公共危机管理案例知识库系统平台,将预测模型成功应用到实际工程领域中。
As a widely discussed issue, there are already a large number of models to solve forecasting problems. In this research, these models can be roughly divided into two categories:time series models and Spatial-temporal Analysis combined with GIS.
The time series forecastiong model mainly contain linear and nonlinear ones. Linear models such as ARMA, ARIMA models can adjust itself according to the new obtained data so as to improve the forecasting accuracy, however, they are unable to analysis all the factors of fluctuations in the data, and thus the forecasting accuracy will be affected. The typical nonlinear model is artificial neural network model,which has a strong parallel computing capabilities. It can implement any complex nonlinear mapping function proved by mathematical theories. It has been widely applied in the field of forecasting, but it is can lead to local minima and over-fitting phenomenon. Chaos Particle Swarm Optimization is an evolutionary computation technique combined with chaos search, it depends on sharing the individual information with the whole group in the problem solving space in order to find the optimal solution and the movement of particles evoluted from disorder to order. So Swarm intelligence technology has been widely applied to optimize fields, which can effectively find the global optimal solution. This study proposed a Chaos Particle Swarm optimization BP neural network prediction model as a basis research model based on the two intelligent calculation methods. This study has paid more attention on these two aspect as follow:1) The parameters selection of the hybrid model. Through a large number of experiments in-depth analysis the performance of the hybrid model of neural network based on smart optimization algorithms, experiment results demonstrate that without carefully chosen parameters, the hybrid forecasting model may not be better than a single forecasting model. So we combine the CPSO-BP neural network with input parameters selection method to achieve better predict performance.2) The data processing of input dataset. We propose a hybrid prediction model by combining wavelet transfonn with the previous study to ensure the predictive ability and the stability of the model.
In the spatial-temporal forecasting analysis, this study presents the idea of projection:the core idea is to simplify complex issues, achieved by the N-dimensional space to a plurality of one-dimensional mapping, not only reduces the computational difficulty, but also reduce the computation time, thereby reducing the time complexity of the algorithm. On this basis, this paper proposes a projection-based hotspot analysis model, then discusses the time and space complexity of the algorithm from the theoretical level. This model contains both the RNNH accurate prediction feature and lower time cost of K-means algorithm.
The target of this study is to build public crisis management case knowledge platform based on the forecasting model.
Based on the above theoretical and applied research background, three models established in this study:1) Considering the uncertainty of the earthquake occurd, the dispersion characteristics of the H1N1outbreak areas and the crime in the place of uncertainty, this research combined projection ideas and hierarchical clustering ideas, and then proposed a projection-based hotspot analysis forcasting model. This model has been successfully applied to Auckland crime analysis, seismic data analysis of the United States, H1N1occurred hotspot analysis and global seismic data analysis.2) The research background was set in energy crisis, back propagation neural network based on particle swam optimization combine with input parameters selcetion model has been applied to the wind speed forecasting, through experimental results, it has been verified the validity of the prediction model.3) Considering the importance of the influenza forecasting, this study used BP neural network based on particle swam optimization combine with wavelet analysis into the United States, Canada, Australia and South Africa influenza forecasting,then described from the prediction accuracy and stability of the algorithm.
The main research achievements and contributions are as follows:
1) Applied set theory in the field of forcasting, and used it as a basis analysis tools for SOA-based public crisis case management platform.
2)In the field of spatial-temporal analysis, proposed projection-based hotspot analysis forecasting model, and successfully applied it into predict infectious disease, crime analysis and seismic hotspots analysis.
3) Researched on two new hybrid forecasting model, then successfully applied them into wind speed prediction and influenza forecasting to verify the validity of the models.
4) Building a SOA-based public crisis knowledge management platform, make the forecasting models successfully applied to the actual engineering field.
时间序列预测模型主要分为线性模型和非线性模型,线性模型代表的方法有ARMA、ARIMA模型,这些模型能够根据数据进行动态调整,实现预测精度的提高,但是它们无法对数据波动的各个因素进行全面分析,因而预测精度会受到影响。非线性模型的典型代表是人工神经网络模型,它具有很强的并行计算能力,且能够以任意精度逼近任何非线性连续函数,因此其在预测领域得到了广泛的应用,但是其容易出现过拟合现象和陷入局部最小值。混沌粒子群算法(Chaos Particle Swarm Optimization,简称CPSO)是一种基于群体智能的进化计算技术并融合了混沌思想,利用群体中的个体对信息的共享使整个群体的运动在问题求解空间中产生从无序到有序的演化过程,从而获得最优解,群体智能技术被广泛地应用到优化领域,其可以有效地寻找全局最优解。因此,本研究以上述两种智能计算方法为基础,提出了以混沌粒子群优化BP神经网络的混合预测模型(CPSO-BP),并从以下两方面深入研究提高模型的预测精度：1)混合模型的参数选择。通过大量实验深入剖析了智能寻优算法与神经网络结合的混合算法的性能,用实验证实了如果模型参数选择不合适,混合模型的预测能力未必比单一预测模型要好,因此在CPSO-BP模型的基础上提出了两种输入样本数据选择方式及预测模型输入参数的确定方法,从而使预测模型达到最优状态。2)样本数据的处理。在前面研究基础上,从样本数据的处理入手,将小波变换的思想融入混合预测模型,进一步发挥模型的预测能力并保证模型的稳定性。
在时空分析预测方面,本研究提出了投影的思想：其核心思想就是将复杂的问题简单化,实现由N维空间向多个一维空间的映射,既降低了计算难度,又降低计算时间,进而降低算法的时间复杂度。本文在此基础上提出了基于投影思想的层次聚类热点分析模型,并从理论层面深入讨论了算法的时空复杂度,该模型既包含了RNNH预测准确的特点,同时在时间复杂度上优于K-means算法。
本研究以公共危机事件预测模型构建为研究对象,已建立面向服务架构的公共危机管理案例知识库系统平台为目标,从理论和实践两方面实现对公共危机管理预测模型的研究。
基于上述研究背景,本研究建立了三个模型应用场景：1)考虑到地震发生的不确定性、H1N1爆发地方的分散特性以及犯罪地点发生的不确定性,本研究将投影思想和层次聚类思想相融合,提出了基于投影的热点分析预测模型,并将该预测模型成功应用到奥克兰犯罪分析、美国地震数据分析、H1Nl发生热点分析以及全球地震数据分析中。2)以能源危机为研究背景切入点,本研究将上述提到的基于参数选择的混沌PSO优化BP神经网络模型应用到风速预测中,通过实验结果分析,验证了预测模型的有效性。3)考虑流感数据预测的重要性,本研究将基于小波分析的混沌PSO优化BP神经网络模型应用到美国、加拿大、澳大利亚和南非四个国家的流感预测中,并从预测准确度和算法稳定性上进行了说明。
本研究的主要成果及贡献如下所示：
1)发展了集合论在预测领域的应用,并将其作为基础分析工具成功应用到基于SOA的公共危机管理案例知识库系统平台中。
2)在时空分析领域中,提出基于投影思想的层次热点分析预测模型,并将此模型成功应用到传染病预测、犯罪分析和地震热点分析中。
3)引入两种新的基于智能计算的模型,并将这两个模型成功应用到风速预测和流感预测中,并对模型的有效性进行了验证。
4)构建了基于SOA的公共危机管理案例知识库系统平台,将预测模型成功应用到实际工程领域中。
As a widely discussed issue, there are already a large number of models to solve forecasting problems. In this research, these models can be roughly divided into two categories:time series models and Spatial-temporal Analysis combined with GIS.
The time series forecastiong model mainly contain linear and nonlinear ones. Linear models such as ARMA, ARIMA models can adjust itself according to the new obtained data so as to improve the forecasting accuracy, however, they are unable to analysis all the factors of fluctuations in the data, and thus the forecasting accuracy will be affected. The typical nonlinear model is artificial neural network model,which has a strong parallel computing capabilities. It can implement any complex nonlinear mapping function proved by mathematical theories. It has been widely applied in the field of forecasting, but it is can lead to local minima and over-fitting phenomenon. Chaos Particle Swarm Optimization is an evolutionary computation technique combined with chaos search, it depends on sharing the individual information with the whole group in the problem solving space in order to find the optimal solution and the movement of particles evoluted from disorder to order. So Swarm intelligence technology has been widely applied to optimize fields, which can effectively find the global optimal solution. This study proposed a Chaos Particle Swarm optimization BP neural network prediction model as a basis research model based on the two intelligent calculation methods. This study has paid more attention on these two aspect as follow:1) The parameters selection of the hybrid model. Through a large number of experiments in-depth analysis the performance of the hybrid model of neural network based on smart optimization algorithms, experiment results demonstrate that without carefully chosen parameters, the hybrid forecasting model may not be better than a single forecasting model. So we combine the CPSO-BP neural network with input parameters selection method to achieve better predict performance.2) The data processing of input dataset. We propose a hybrid prediction model by combining wavelet transfonn with the previous study to ensure the predictive ability and the stability of the model.
In the spatial-temporal forecasting analysis, this study presents the idea of projection:the core idea is to simplify complex issues, achieved by the N-dimensional space to a plurality of one-dimensional mapping, not only reduces the computational difficulty, but also reduce the computation time, thereby reducing the time complexity of the algorithm. On this basis, this paper proposes a projection-based hotspot analysis model, then discusses the time and space complexity of the algorithm from the theoretical level. This model contains both the RNNH accurate prediction feature and lower time cost of K-means algorithm.
The target of this study is to build public crisis management case knowledge platform based on the forecasting model.
Based on the above theoretical and applied research background, three models established in this study:1) Considering the uncertainty of the earthquake occurd, the dispersion characteristics of the H1N1outbreak areas and the crime in the place of uncertainty, this research combined projection ideas and hierarchical clustering ideas, and then proposed a projection-based hotspot analysis forcasting model. This model has been successfully applied to Auckland crime analysis, seismic data analysis of the United States, H1N1occurred hotspot analysis and global seismic data analysis.2) The research background was set in energy crisis, back propagation neural network based on particle swam optimization combine with input parameters selcetion model has been applied to the wind speed forecasting, through experimental results, it has been verified the validity of the prediction model.3) Considering the importance of the influenza forecasting, this study used BP neural network based on particle swam optimization combine with wavelet analysis into the United States, Canada, Australia and South Africa influenza forecasting,then described from the prediction accuracy and stability of the algorithm.
The main research achievements and contributions are as follows:
1) Applied set theory in the field of forcasting, and used it as a basis analysis tools for SOA-based public crisis case management platform.
2)In the field of spatial-temporal analysis, proposed projection-based hotspot analysis forecasting model, and successfully applied it into predict infectious disease, crime analysis and seismic hotspots analysis.
3) Researched on two new hybrid forecasting model, then successfully applied them into wind speed prediction and influenza forecasting to verify the validity of the models.
4) Building a SOA-based public crisis knowledge management platform, make the forecasting models successfully applied to the actual engineering field.
引文
[1]秦麟征.预测科学:未来研究学[M].方志出版社,2007.
[2]袁政.公共管理定量分析:方法与技术[M].重庆大学出版社,2006.
[3]赵丽芬.管理学教程[M].立信会计出版社,2006.
[4]洪毅,林健良,&陶志穗.数学模型[M].高等教育出版社,2004.
[5]牛晓东,曹树华,&赵磊,电力负荷预测技术及其应用.Publishing,1998.
[6]徐秋栋.工业工程实践案例及方法.工业工程与管理,14,84-84,2009.
[7]Mohandes, M.A., Rehman, S.,& Halawani, T.O. A neural networks approach for wind speed prediction. Renewable Energy,13,345-354,1998.
[8]Yu, L., Wang, S.,& Lai, K.K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics,30,2623-2635,2008.
[9]Kennedy, J.,& Eberhart, R., Particle swarm optimization, IEEE International of first Conference on Neural Networks. Publishing,1995.
[10]Eberhart, R.,& Kennedy, J., A new optimizer using particle swarm theory, Micro Machine and Human Science,1995. MHS'95., Proceedings of the Sixth International Symposium on. Publishing, pp.39-43,1995.
[11]Pulido, G.T.,& Coello, C.C., A constraint-handling mechanism for particle swarm optimization, Evolutionary Computation,2004. CEC2004. Congress on. Publishing, pp.1396-1403,2004.
[12]Carlisle, A.,& Dozier, G., Adapting particle swarm optimization to dynamic environments, Proceedings of the International Conference on Artificial Intelligence. Publishing, pp.429-434, 2000.
[13]Eberhart, R.C.,& Shi, Y., Tracking and optimizing dynamic systems with particle swarms, Evolutionary Computation,2001. Proceedings of the 2001 Congress on. Publishing, pp.94-100, 2001.
[14]Hu, X.,& Eberhart, R.C., Adaptive particle swarm optimization:detection and response to dynamic systems, Evolutionary Computation,2002. CEC'02. Proceedings of the 2002 Congress on. Publishing, pp.1666-1670,2002.
[15]Reyes-Sierra, M.,& Coello, C.C. Multi-objective particle swarm optimizers:A survey of the state-of-the-art. International Journal of Computational Intelligence Research,2,287-308, 2006.
[16]del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.-C.,& Harley, R.G. Particle swarm optimization:basic concepts, variants and applications in power systems. Evolutionary Computation, IEEE Transactions on,12,171-195,2008.
[17]Storn, R. Designing nonstandard filters with differential evolution. Signal Processing Magazine, IEEE,22,103-106,2005.
[18]Heo, J.S., Lee, K.Y.,& Garduno-Ramirez, R. Multiobjective control of power plants using particle swarm optimization techniques. Energy Conversion, IEEE Transactions on,21,552-561,2006.
[19]Mukherjee, V.,& Ghoshal, S. Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electric Power Systems Research,77,1689-1698,2007.
[20]Abraham, A., Das, S.,& Roy, S., Swarm intelligence algorithms for data clustering, Soft Computing for Knowledge Discovery and Data Mining. Publishing, pp.279-313,2008.
[21]Gudise, V.G.,& Venayagamoorthy, G.K., Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks, Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE. Publishing, pp.110-117,2003.
[22]Wachowiak, M.P., Smolikovd, R., Zheng, Y., Zurada, J.M.,& Elmaghraby, A.S. An approach to multimodal biomedical image registration utilizing particle swarm optimization. Evolutionary Computation, IEEE Transactions on,8,289-301,2004.
[23]Costa Jr, E., Lage, P.,& Biscaia Jr, E. On the numerical solution and optimization of styrene polymerization in tubular reactors. Computers & chemical engineering,27,1591-1604,2003.
[24]Park, J., Choi, K.,& Allstot, D.J. Parasitic-aware RF circuit design and optimization. Circuits and Systems Ⅰ:Regular Papers, IEEE Transactions on,51,1953-1966,2004.
[25]Pavlidis, N., Parsopoulos, K.E.,& Vrahatis, M.N. Computing Nash equilibria through computational intelligence methods. Journal of Computational and Applied Mathematics,175, 113-136,2005.
[26]Rasmussen, T.K.,& Krink, T. Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid. Biosystems,72, 5-17,2003.
[27]Shen, Q., Jiang, J.-H., Jiao, C.-X., Shen, G.-l.,& Yu, R.-Q. Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling:QSAR studies of antagonism of angiotensin II antagonists. European Journal of Pharmaceutical Sciences,22,145-152,2004.
[28]Littlewood, J.E.,& Paley, R.E. Theorems on Fourier series and power series. Journal of the London Mathematical Society,1,230-233,1931.
[29]Mallat, S.G. A theory for multiresolution signal decomposition:the wavelet representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on,11,674-693,1989.
[30]Cao, L., Hong, Y., Fang, H.,& He, G. Predicting chaotic time series with wavelet networks. Physica D:Nonlinear Phenomena,85,225-238,1995.
[31]Aussem, A.,& Murtagh, F. Combining neural network forecasts on wavelet-transformed time series. Connection Science,9,113-122,1997.
[32]Aussem, A., Campbell, J.,& Murtagh, F. Wavelet-based feature extraction and decomposition strategies for financial forecasting. Journal of Computational Intelligence in Finance,6,5-12, 1998.
[33]陈树文.领导学[M].清华大学出版社,2011.
[34]肖鹏军.公共危机管理导论[M].中国人民大学出版社,2006.
[35]World Health Organization (2009) Influenza (Seasonal); 2009. Available:http://www.who.int/mediacentre/factsheets/fs211/en/. Accessed March 24th,2013.
[36]Massad, E., Burattini, M.N., Lopez, L.F.,& Coutinho, F.A. Forecasting versus projection models in epidemiology:the case of the SARS epidemics. Medical hypotheses,65,17-22,2005.
[37]Hall, I., Gani, R., Hughes, H.,& Leach, S. Real-time epidemic forecasting for pandemic influenza. Epidemiology and infection,135,372-385,2007.
[38]Nishiura, H. Real-time forecasting of an epidemic using a discrete time stochastic model:a case study of pandemic influenza (H1N1-2009). Biomed Eng Online,10,15,2011.
[39]Hsieh, Y.H. Pandemic influenza A (H1N1) during winter influenza season in the southern hemisphere. Influenza and other respiratory viruses,4,187-197,2010.
[40]Hsieh, Y.-H.,& Cheng, Y.-S. Real-time forecast of multiphase outbreak. Emerging infectious diseases,12,122,2006.
[41]LONGINI, I.M., FINE, P.E.,& THACKER, S.B. Predicting the global spread of new infectious agents. American Journal of Epidemiology,123,383-391,1986.
[42]Birrell, P.J., Ketsetzis, G., Gay, N J., Cooper, B.S., Presanis, A.M., Harris, R.J., Charlett, A., Zhang, X.-S., White, P.J.,& Pebody, R.G. Bayesian modeling to unmask and predict influenza A/HlNlpdm dynamics in London. Proceedings of the National Academy of Sciences,108, 18238-18243,2011.
[43]Ong, J.B.S., Mark, I., Chen, C., Cook, A.R., Lee, H.C., Lee, V.J., Lin, R.T.P., Tambyah, P.A.,& Goh, L.G. Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PloS one,5, e10036,2010.
[44]Dukic, V., Lopes, H.,& Poison, N. Tracking flu epidemics using google flu trends and particle learning. Available at SSRN 15137052009.
[45]Dukic, V., Lopes, H.F.,& Poison, N.G. Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model. Journal of the American Statistical Association,107,1410-1426, 2012.
[46]Soebiyanto, R.P., Adimi, F.,& Kiang, R.K. Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS One,5, e9450,2010.
[47]WANG, X., CHENG, X.-w.,& FANG, S.-s. Prediction of influenza incidence by using ARIMA. China Tropical Medicine,6,005,2011.
[48]Meng, L., Wang, X., Li, J., Ren, X., Li, H., Hu, X., Yang, X., Qin, L., Chen, J.,& Bai, Y. [Dynamic prediction on the number of influenza-like cases in Gansu province based on data from the influenza sentinel surveillance program, from 2006 to 2011]. Zhonghua liu xing bing xue za zhi=Zhonghua liuxingbingxue zazhi,33,1155-1158,2012.
[49]Trtica-Majnaric, L., Zekic-Susac, M., Sarlija, N.,& Vitale, B. Prediction of influenza vaccination outcome by neural networks and logistic regression. Journal of biomedical informatics,43, 774-781,2010.
[50]Qiang, X.,& Kou, Z. Prediction of interspecies transmission for avian influenza A virus based on a back-propagation neural network. Mathematical and Computer Modelling,52,2060-2065, 2010.
[51]Mao, Y., A study on artificial neural networks-based fever patient number forecast, Computer Application and System Modeling (ICCASM),2010 International Conference on. Publishing, pp.V9-163-V169-165,2010.
[52]Xu, W., Han, Z.-W.,& Ma, J., A neural netwok based approach to detect influenza epidemics using search engine query data, Machine Learning and Cybernetics (ICMLC),2010 International Conference on. Publishing, pp.1408-1412,2010.
[53]武继磊,王劲峰,郑晓瑛,宋新明,孟斌,&张科利.空间数据分析技术在公共卫生领域的应用.地理科学进展,22,219-228,2003.
[54]Nisha, V., Gad, S.S., Selvapandian, D., Suganya, V., Rajagopal, V., Suganti, P., Balraj, V.,& Devasundaram, J. Geographical information system (GIS) in investigation of an outbreak. The Journal of communicable diseases,37,39,2005.
[55]Levine, N. CrimeStat III:A Spatial Statistics Program for the Analysis of Crime Incident Locations (version 3.1), Ned Levine & Associates, Houston, National Institute of Justice, Washington. TX/National Institute of Justice:Washington, DC2004.
[56]Alexiadis, M., Dokopoulos, P., Sahsamanoglou, H.,& Manousaridis, I. Short-term forecasting of wind speed and related electrical power. Solar Energy,63,61-68,1998.
[57]Bossanyi, E. Short-term wind prediction using Kalman filters. Wind Engineering,9,1-8,1985.
[58]Kamal, L.,& Jafri, Y.Z. Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan. Solar Energy,61,23-32,1997.
[59]Li, H.-Z., Guo, S., Li, C.-j.,& Sun, J.-q. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems2012.
[60]Bashir, Z.,& El-Hawary, M. Applying wavelets to short-term load forecasting using PSO-based neural networks. Power Systems, IEEE Transactions on,24,20-27,2009.
[61]Xiao, Z., Ye, S.-J., Zhong, B.,& Sun, C.-X. BP neural network with rough set for short term load forecasting. Expert Systems with Applications,36,273-279,2009.
[62]Cadenas, E.,& Rivera, W. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model. Renewable Energy,35,2732-2738,2010.
[63]Damousis, I.G.,& Dokopoulos, P., A fuzzy expert system for the forecasting of wind speed and power generation in wind farms, Power Industry Computer Applications,2001. PICA 2001. Innovative Computing for Power-Electric Energy Meets the Market.22nd IEEE Power Engineering Society International Conference on. Publishing, pp.63-69,2001.
[64]Bioportal; 2009. Available:http://www.bioportal.org/. Accessed June 30th,2009.
[65]Zeng, D., Chen, H., Tseng, C., Larson, C, Eidson, M., Gotham, I., Lynch, C.,& Ascher, M., West Nile virus and botulism portal:a case study in infectious disease informatics, Intelligence and Security Informatics. Publishing, pp.28-41,2004.
[66]Mohan, P., Wilson, R.E., Shekhar, S., George, B., Levine, N.,& Celik, M., Should SDBMS support a join index?:a case study from CrimeStat, Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems. Publishing, p.37, 2008.
[67]McCullagh, M.J. Detecting hotspots in time and space. ISG062006.
[68]Kulldorff, M. A spatial scan statistic. Communications in Statistics-Theory and methods,26, 1481-1496,1997.
[69]Wang, W., Yang, J.,& Muntz, R., STING:A statistical information grid approach to spatial data mining, VLDB. Publishing, pp.186-195,1997.
[70]Sheikholeslami, G., Chatterjee, S.,& Zhang, A., Wavecluster:A multi-resolution clustering approach for very large spatial databases, VLDB. Publishing, pp.428-439,1998.
[71]Agrawal, R., Gehrke, J., Gunopulos, D.,& Raghavan, P. Automatic subspace clustering of high dimensional data for data mining applications Vol.27 [M]. ACM,1998.
[72]Jain, A.K. Data clustering:50 years beyond K-means. Pattern Recognition Letters,31,651-666, 2010.
[73]Johnson, S.C. Hierarchical clustering schemes. Psychometrika,32,241-254,1967.
[74]Levine, N., CrimeStat:A Spatial Statistical Program for the Analysis of Crime Incidents, Encyclopedia of GIS. Publishing, pp.187-193,2008.
[75]Jain, A.K., Murty, M.N.,& Flynn, P.J. Data clustering:a review. ACM computing surveys (CSUR), 31,264-323,1999.
[76]Agung, A.A.G. Crime Hot Spot Analysis and Dynamic Pin Map. Spatial and Temporal Analysis of Crime:User Manual Illinois:Criminal Justice Information Authority1993.
[77]中国政府危机新闻网站2009. Available:http://www.gov.cn/yjgl/tfsj.htm.Accessed October 8th, 2013.
[78]USGS Earthquake Global Search Homepage; 2009. Available:http://earthquake.usgs.gov/earthquakes/search/.Accessed October 8th,2013.
[79]FluTracker 2009. Available:http://flutracker.rhizalabs.com/. Accessed June 30th,2009.
[80]Oakland Crime Maps; 2009. Available:http://mike.teczno.com/notes/oakland-crime-maps/Ⅱ.html. Accessed October 8th,2013.
[81]Berkhin, P., A survey of clustering data mining techniques, Grouping multidimensional data. Publishing, pp.25-71,2006.
[82]Box, G. Box and Jenkins:Time Series Analysis, Forecasting and Control. A Very British Affair～ autofilled～,161,1994.
[83]Kavasseri, R.G.,& Seetharaman, K. Day-ahead wind speed forecasting using< i> f-ARIMA models. Renewable Energy,34,1388-1393,2009.
[84]Rumelhart, D.E., Hintont, G.E.,& Williams, R.J. Learning representations by back-propagating errors. Nature,323,533-536,1986.
[85]Zhang, L., Wu, K., Zhong, Y.,& Li, P. A new sub-pixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing,71,2046-2054,2008.
[86]Tetko, I.V., Livingstone, D.J.,& Luik, A.I. Neural network studies. I. Comparison of overfitting and overtraining. Journal of chemical information and computer sciences,35,826-833,1995.
[87]Li, G.,& Shi, J. On comparing three artificial neural networks for wind speed forecasting. Applied Energy,87,2313-2320,2010.
[88]Erdogdu, E. Electricity demand analysis using cointegration and ARIMA modelling:A case study of Turkey. Energy policy,35,1129-1146,2007.
[89]ARIMA model; 2011. Available:http://www.scss.tcd.ie/Rozenn.Dahyot/ST7005/14ARIMA.pdf/. Accessed September 17th,2013.
[90]Wang, H.,& Zhao, W., Arima model estimated by particle swarm optimization algorithm for consumer price index forecasting, Artificial Intelligence and Computational Intelligence. Publishing, pp.48-58,2009.
[91]Ong, C.-S., Huang, J.-J.,& Tzeng, G.-H. Model identification of ARIMA family using genetic algorithms. Applied Mathematics and Computation,164,885-912,2005.
[92]Ediger, V.S., Akar, S.,& Ugurlu, B. Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model. Energy Policy,34,3836-3846,2006.
[93]Hecht-Nielsen, R., Kolmogorov's mapping neural network existence theorem, Proceedings of the international conference on Neural Networks. Publishing,1987.
[94]Ozcan, E.,& Mohan, C.K., Particle swarm optimization:surfing the waves, Evolutionary Computation,1999. CEC 99. Proceedings of the 1999 Congress on. Publishing,1999.
[95]Shi, Y., Particle swarm optimization:developments, applications and resources, Evolutionary Computation,2001. Proceedings of the 2001 Congress on. Publishing, pp.81-86,2001.
[96]Kennedy, J.F., Kennedy, J.,& Eberhart, R.C. Swarm intelligence[M]. Morgan Kaufmann Pub, 2001.
[97]Shi, Y.,& Eberhart, R.C., Empirical study of particle swarm optimization, Evolutionary Computation,1999. CEC 99. Proceedings of the 1999 Congress on. Publishing,1999.
[98]刘军民,&高岳林.混沌粒子群优化算法.计算机应用,28,322-325,2008.
[99]Krink, T., VesterstrOm, J.S.,& Riget, J., Particle swarm optimisation with spatial particle extension, Evolutionary Computation,2002. CEC'02. Proceedings of the 2002 Congress on. Publishing, pp.1474-1479,2002.
[100]吕振肃,&侯志荣.自适应变异的粒子群优化算法.电子学报,32,416-420,2004.
[101]Guo, Z., Dong, Y., Wang, J.,& Lu, H. The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area. Mathematical Problems in Engineering,20102010.
[102]Introduction of Jiuquan city on Wikipedia; 2013. Available:http://zh.wikipedia.org/wiki/%E9%85%92%E6%B3%89%E5%B8%82. Accessed May 12th,2013.
[103]The Central People's Government of the people's Republic of China; 2013. Available:http://www.gov.cn/jrzg/2009-08/08/content 1386664.htm. Accessed May 12th, 2013.
[104]Ediger, V.S.,& Akar, S. ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy,35,1701-1708,2007.
[105]张得丰.MATLAB小波分析[M].机械工业出版社,2009.
[106]Zhang, Z., Lu, C., Zhang, F., Ren, Y., Yang, K.,& Su, Z. A Novel Method for non-contact measuring diameter parameters of wheelset based on wavelet analysis. Optik-International Journal for Light and Electron Optics,123,433-438,2012.
[107]Dugas, A.F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Igusa, T.,& Rothman, R.E. Influenza Forecasting with Google Flu Trends. PloSone,8, e56176,2013.
[108]Zweighaft, R.M., Fraser, D.W., Hattwick, M., Winkler, W.G., Jordan, W.C., Alter, M., Wolfe, M., Wulff, H.,& Johnson, K.M. Lassa fever:response to an imported case. The New England journal of medicine,297,803,1977.
[109]Centers for Disease Control and Prevention (Overview of Influenza Surveillance in the United States). Available:http://www.cdc.gov/flu/weekly/overview.htm.Accessed March 25th,2013.
[110]Public Health Agency of Canada; Available:http://www.phac-aspc.gc.ca/index-eng.php Accessed March 25th,2013.
[111]Victorian Infectious Diseases Reference Laboratory; Available:http://www.vidrl.org.au/. Accessed March 25th,2013.
[112]National Institute for Communicable Diseases; Available:http://www.nicd.ac.za/. Accessed March 25th,2013.
[113]European Influenza Surveillance Network of the European Centre for Disease Prevention and Control; Available:http://ecdc.europa.eu/en/activities/surveillance/EISN/Pages/index.aspx. Accessed March 25th,2013.
[114]National Institute of Infectious Diseases; Available:http://www.nih.go.jp/niid/.Accessed March 25th,2013.
[115]Signorini, A., Segre, A.M.,& Polgreen, P.M. The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PloS one,6, e19467, 2011.
[116]Culotta, A. Detecting influenza outbreaks by analyzing Twitter messages. arXiv preprint arXiv:1007.47482010.
[117]Google (2013) Google Flu Trends; Available:http://www.google.org/flutrends. Accessed March 25th,2013.
[118]Carneiro, H.A.,& Mylonakis, E. Google trends:a web-based tool for real-time surveillance of disease outbreaks. Clinical infectious diseases,49,1557-1564,2009.
[119]Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S.,& Brilliant, L. Detecting influenza epidemics using search engine query data. Nature 2009,7232,2009.
[120]Ortiz, J.R., Zhou, H., Shay, D.K., Neuzil, K.M., Fowlkes, A.L.,& Goss, C.H. Monitoring influenza activity in the United States:a comparison of traditional surveillance systems with Google Flu Trends. PLoS One,6, e18687,2011.
[121]Cook, S., Conrad, C., Fowlkes, A.L.,& Mohebbi, M.H. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One,6, e23610, 2011.
[122]罗伯特·希斯著,王成,宋炳辉,金瑛译.危机管理[M].中信出版社,2001.
[2]袁政.公共管理定量分析:方法与技术[M].重庆大学出版社,2006.
[3]赵丽芬.管理学教程[M].立信会计出版社,2006.
[4]洪毅,林健良,&陶志穗.数学模型[M].高等教育出版社,2004.
[5]牛晓东,曹树华,&赵磊,电力负荷预测技术及其应用.Publishing,1998.
[6]徐秋栋.工业工程实践案例及方法.工业工程与管理,14,84-84,2009.
[7]Mohandes, M.A., Rehman, S.,& Halawani, T.O. A neural networks approach for wind speed prediction. Renewable Energy,13,345-354,1998.
[8]Yu, L., Wang, S.,& Lai, K.K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics,30,2623-2635,2008.
[9]Kennedy, J.,& Eberhart, R., Particle swarm optimization, IEEE International of first Conference on Neural Networks. Publishing,1995.
[10]Eberhart, R.,& Kennedy, J., A new optimizer using particle swarm theory, Micro Machine and Human Science,1995. MHS'95., Proceedings of the Sixth International Symposium on. Publishing, pp.39-43,1995.
[11]Pulido, G.T.,& Coello, C.C., A constraint-handling mechanism for particle swarm optimization, Evolutionary Computation,2004. CEC2004. Congress on. Publishing, pp.1396-1403,2004.
[12]Carlisle, A.,& Dozier, G., Adapting particle swarm optimization to dynamic environments, Proceedings of the International Conference on Artificial Intelligence. Publishing, pp.429-434, 2000.
[13]Eberhart, R.C.,& Shi, Y., Tracking and optimizing dynamic systems with particle swarms, Evolutionary Computation,2001. Proceedings of the 2001 Congress on. Publishing, pp.94-100, 2001.
[14]Hu, X.,& Eberhart, R.C., Adaptive particle swarm optimization:detection and response to dynamic systems, Evolutionary Computation,2002. CEC'02. Proceedings of the 2002 Congress on. Publishing, pp.1666-1670,2002.
[15]Reyes-Sierra, M.,& Coello, C.C. Multi-objective particle swarm optimizers:A survey of the state-of-the-art. International Journal of Computational Intelligence Research,2,287-308, 2006.
[16]del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.-C.,& Harley, R.G. Particle swarm optimization:basic concepts, variants and applications in power systems. Evolutionary Computation, IEEE Transactions on,12,171-195,2008.
[17]Storn, R. Designing nonstandard filters with differential evolution. Signal Processing Magazine, IEEE,22,103-106,2005.
[18]Heo, J.S., Lee, K.Y.,& Garduno-Ramirez, R. Multiobjective control of power plants using particle swarm optimization techniques. Energy Conversion, IEEE Transactions on,21,552-561,2006.
[19]Mukherjee, V.,& Ghoshal, S. Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electric Power Systems Research,77,1689-1698,2007.
[20]Abraham, A., Das, S.,& Roy, S., Swarm intelligence algorithms for data clustering, Soft Computing for Knowledge Discovery and Data Mining. Publishing, pp.279-313,2008.
[21]Gudise, V.G.,& Venayagamoorthy, G.K., Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks, Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE. Publishing, pp.110-117,2003.
[22]Wachowiak, M.P., Smolikovd, R., Zheng, Y., Zurada, J.M.,& Elmaghraby, A.S. An approach to multimodal biomedical image registration utilizing particle swarm optimization. Evolutionary Computation, IEEE Transactions on,8,289-301,2004.
[23]Costa Jr, E., Lage, P.,& Biscaia Jr, E. On the numerical solution and optimization of styrene polymerization in tubular reactors. Computers & chemical engineering,27,1591-1604,2003.
[24]Park, J., Choi, K.,& Allstot, D.J. Parasitic-aware RF circuit design and optimization. Circuits and Systems Ⅰ:Regular Papers, IEEE Transactions on,51,1953-1966,2004.
[25]Pavlidis, N., Parsopoulos, K.E.,& Vrahatis, M.N. Computing Nash equilibria through computational intelligence methods. Journal of Computational and Applied Mathematics,175, 113-136,2005.
[26]Rasmussen, T.K.,& Krink, T. Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid. Biosystems,72, 5-17,2003.
[27]Shen, Q., Jiang, J.-H., Jiao, C.-X., Shen, G.-l.,& Yu, R.-Q. Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling:QSAR studies of antagonism of angiotensin II antagonists. European Journal of Pharmaceutical Sciences,22,145-152,2004.
[28]Littlewood, J.E.,& Paley, R.E. Theorems on Fourier series and power series. Journal of the London Mathematical Society,1,230-233,1931.
[29]Mallat, S.G. A theory for multiresolution signal decomposition:the wavelet representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on,11,674-693,1989.
[30]Cao, L., Hong, Y., Fang, H.,& He, G. Predicting chaotic time series with wavelet networks. Physica D:Nonlinear Phenomena,85,225-238,1995.
[31]Aussem, A.,& Murtagh, F. Combining neural network forecasts on wavelet-transformed time series. Connection Science,9,113-122,1997.
[32]Aussem, A., Campbell, J.,& Murtagh, F. Wavelet-based feature extraction and decomposition strategies for financial forecasting. Journal of Computational Intelligence in Finance,6,5-12, 1998.
[33]陈树文.领导学[M].清华大学出版社,2011.
[34]肖鹏军.公共危机管理导论[M].中国人民大学出版社,2006.
[35]World Health Organization (2009) Influenza (Seasonal); 2009. Available:http://www.who.int/mediacentre/factsheets/fs211/en/. Accessed March 24th,2013.
[36]Massad, E., Burattini, M.N., Lopez, L.F.,& Coutinho, F.A. Forecasting versus projection models in epidemiology:the case of the SARS epidemics. Medical hypotheses,65,17-22,2005.
[37]Hall, I., Gani, R., Hughes, H.,& Leach, S. Real-time epidemic forecasting for pandemic influenza. Epidemiology and infection,135,372-385,2007.
[38]Nishiura, H. Real-time forecasting of an epidemic using a discrete time stochastic model:a case study of pandemic influenza (H1N1-2009). Biomed Eng Online,10,15,2011.
[39]Hsieh, Y.H. Pandemic influenza A (H1N1) during winter influenza season in the southern hemisphere. Influenza and other respiratory viruses,4,187-197,2010.
[40]Hsieh, Y.-H.,& Cheng, Y.-S. Real-time forecast of multiphase outbreak. Emerging infectious diseases,12,122,2006.
[41]LONGINI, I.M., FINE, P.E.,& THACKER, S.B. Predicting the global spread of new infectious agents. American Journal of Epidemiology,123,383-391,1986.
[42]Birrell, P.J., Ketsetzis, G., Gay, N J., Cooper, B.S., Presanis, A.M., Harris, R.J., Charlett, A., Zhang, X.-S., White, P.J.,& Pebody, R.G. Bayesian modeling to unmask and predict influenza A/HlNlpdm dynamics in London. Proceedings of the National Academy of Sciences,108, 18238-18243,2011.
[43]Ong, J.B.S., Mark, I., Chen, C., Cook, A.R., Lee, H.C., Lee, V.J., Lin, R.T.P., Tambyah, P.A.,& Goh, L.G. Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PloS one,5, e10036,2010.
[44]Dukic, V., Lopes, H.,& Poison, N. Tracking flu epidemics using google flu trends and particle learning. Available at SSRN 15137052009.
[45]Dukic, V., Lopes, H.F.,& Poison, N.G. Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model. Journal of the American Statistical Association,107,1410-1426, 2012.
[46]Soebiyanto, R.P., Adimi, F.,& Kiang, R.K. Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS One,5, e9450,2010.
[47]WANG, X., CHENG, X.-w.,& FANG, S.-s. Prediction of influenza incidence by using ARIMA. China Tropical Medicine,6,005,2011.
[48]Meng, L., Wang, X., Li, J., Ren, X., Li, H., Hu, X., Yang, X., Qin, L., Chen, J.,& Bai, Y. [Dynamic prediction on the number of influenza-like cases in Gansu province based on data from the influenza sentinel surveillance program, from 2006 to 2011]. Zhonghua liu xing bing xue za zhi=Zhonghua liuxingbingxue zazhi,33,1155-1158,2012.
[49]Trtica-Majnaric, L., Zekic-Susac, M., Sarlija, N.,& Vitale, B. Prediction of influenza vaccination outcome by neural networks and logistic regression. Journal of biomedical informatics,43, 774-781,2010.
[50]Qiang, X.,& Kou, Z. Prediction of interspecies transmission for avian influenza A virus based on a back-propagation neural network. Mathematical and Computer Modelling,52,2060-2065, 2010.
[51]Mao, Y., A study on artificial neural networks-based fever patient number forecast, Computer Application and System Modeling (ICCASM),2010 International Conference on. Publishing, pp.V9-163-V169-165,2010.
[52]Xu, W., Han, Z.-W.,& Ma, J., A neural netwok based approach to detect influenza epidemics using search engine query data, Machine Learning and Cybernetics (ICMLC),2010 International Conference on. Publishing, pp.1408-1412,2010.
[53]武继磊,王劲峰,郑晓瑛,宋新明,孟斌,&张科利.空间数据分析技术在公共卫生领域的应用.地理科学进展,22,219-228,2003.
[54]Nisha, V., Gad, S.S., Selvapandian, D., Suganya, V., Rajagopal, V., Suganti, P., Balraj, V.,& Devasundaram, J. Geographical information system (GIS) in investigation of an outbreak. The Journal of communicable diseases,37,39,2005.
[55]Levine, N. CrimeStat III:A Spatial Statistics Program for the Analysis of Crime Incident Locations (version 3.1), Ned Levine & Associates, Houston, National Institute of Justice, Washington. TX/National Institute of Justice:Washington, DC2004.
[56]Alexiadis, M., Dokopoulos, P., Sahsamanoglou, H.,& Manousaridis, I. Short-term forecasting of wind speed and related electrical power. Solar Energy,63,61-68,1998.
[57]Bossanyi, E. Short-term wind prediction using Kalman filters. Wind Engineering,9,1-8,1985.
[58]Kamal, L.,& Jafri, Y.Z. Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan. Solar Energy,61,23-32,1997.
[59]Li, H.-Z., Guo, S., Li, C.-j.,& Sun, J.-q. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems2012.
[60]Bashir, Z.,& El-Hawary, M. Applying wavelets to short-term load forecasting using PSO-based neural networks. Power Systems, IEEE Transactions on,24,20-27,2009.
[61]Xiao, Z., Ye, S.-J., Zhong, B.,& Sun, C.-X. BP neural network with rough set for short term load forecasting. Expert Systems with Applications,36,273-279,2009.
[62]Cadenas, E.,& Rivera, W. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model. Renewable Energy,35,2732-2738,2010.
[63]Damousis, I.G.,& Dokopoulos, P., A fuzzy expert system for the forecasting of wind speed and power generation in wind farms, Power Industry Computer Applications,2001. PICA 2001. Innovative Computing for Power-Electric Energy Meets the Market.22nd IEEE Power Engineering Society International Conference on. Publishing, pp.63-69,2001.
[64]Bioportal; 2009. Available:http://www.bioportal.org/. Accessed June 30th,2009.
[65]Zeng, D., Chen, H., Tseng, C., Larson, C, Eidson, M., Gotham, I., Lynch, C.,& Ascher, M., West Nile virus and botulism portal:a case study in infectious disease informatics, Intelligence and Security Informatics. Publishing, pp.28-41,2004.
[66]Mohan, P., Wilson, R.E., Shekhar, S., George, B., Levine, N.,& Celik, M., Should SDBMS support a join index?:a case study from CrimeStat, Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems. Publishing, p.37, 2008.
[67]McCullagh, M.J. Detecting hotspots in time and space. ISG062006.
[68]Kulldorff, M. A spatial scan statistic. Communications in Statistics-Theory and methods,26, 1481-1496,1997.
[69]Wang, W., Yang, J.,& Muntz, R., STING:A statistical information grid approach to spatial data mining, VLDB. Publishing, pp.186-195,1997.
[70]Sheikholeslami, G., Chatterjee, S.,& Zhang, A., Wavecluster:A multi-resolution clustering approach for very large spatial databases, VLDB. Publishing, pp.428-439,1998.
[71]Agrawal, R., Gehrke, J., Gunopulos, D.,& Raghavan, P. Automatic subspace clustering of high dimensional data for data mining applications Vol.27 [M]. ACM,1998.
[72]Jain, A.K. Data clustering:50 years beyond K-means. Pattern Recognition Letters,31,651-666, 2010.
[73]Johnson, S.C. Hierarchical clustering schemes. Psychometrika,32,241-254,1967.
[74]Levine, N., CrimeStat:A Spatial Statistical Program for the Analysis of Crime Incidents, Encyclopedia of GIS. Publishing, pp.187-193,2008.
[75]Jain, A.K., Murty, M.N.,& Flynn, P.J. Data clustering:a review. ACM computing surveys (CSUR), 31,264-323,1999.
[76]Agung, A.A.G. Crime Hot Spot Analysis and Dynamic Pin Map. Spatial and Temporal Analysis of Crime:User Manual Illinois:Criminal Justice Information Authority1993.
[77]中国政府危机新闻网站2009. Available:http://www.gov.cn/yjgl/tfsj.htm.Accessed October 8th, 2013.
[78]USGS Earthquake Global Search Homepage; 2009. Available:http://earthquake.usgs.gov/earthquakes/search/.Accessed October 8th,2013.
[79]FluTracker 2009. Available:http://flutracker.rhizalabs.com/. Accessed June 30th,2009.
[80]Oakland Crime Maps; 2009. Available:http://mike.teczno.com/notes/oakland-crime-maps/Ⅱ.html. Accessed October 8th,2013.
[81]Berkhin, P., A survey of clustering data mining techniques, Grouping multidimensional data. Publishing, pp.25-71,2006.
[82]Box, G. Box and Jenkins:Time Series Analysis, Forecasting and Control. A Very British Affair～ autofilled～,161,1994.
[83]Kavasseri, R.G.,& Seetharaman, K. Day-ahead wind speed forecasting using< i> f-ARIMA models. Renewable Energy,34,1388-1393,2009.
[84]Rumelhart, D.E., Hintont, G.E.,& Williams, R.J. Learning representations by back-propagating errors. Nature,323,533-536,1986.
[85]Zhang, L., Wu, K., Zhong, Y.,& Li, P. A new sub-pixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing,71,2046-2054,2008.
[86]Tetko, I.V., Livingstone, D.J.,& Luik, A.I. Neural network studies. I. Comparison of overfitting and overtraining. Journal of chemical information and computer sciences,35,826-833,1995.
[87]Li, G.,& Shi, J. On comparing three artificial neural networks for wind speed forecasting. Applied Energy,87,2313-2320,2010.
[88]Erdogdu, E. Electricity demand analysis using cointegration and ARIMA modelling:A case study of Turkey. Energy policy,35,1129-1146,2007.
[89]ARIMA model; 2011. Available:http://www.scss.tcd.ie/Rozenn.Dahyot/ST7005/14ARIMA.pdf/. Accessed September 17th,2013.
[90]Wang, H.,& Zhao, W., Arima model estimated by particle swarm optimization algorithm for consumer price index forecasting, Artificial Intelligence and Computational Intelligence. Publishing, pp.48-58,2009.
[91]Ong, C.-S., Huang, J.-J.,& Tzeng, G.-H. Model identification of ARIMA family using genetic algorithms. Applied Mathematics and Computation,164,885-912,2005.
[92]Ediger, V.S., Akar, S.,& Ugurlu, B. Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model. Energy Policy,34,3836-3846,2006.
[93]Hecht-Nielsen, R., Kolmogorov's mapping neural network existence theorem, Proceedings of the international conference on Neural Networks. Publishing,1987.
[94]Ozcan, E.,& Mohan, C.K., Particle swarm optimization:surfing the waves, Evolutionary Computation,1999. CEC 99. Proceedings of the 1999 Congress on. Publishing,1999.
[95]Shi, Y., Particle swarm optimization:developments, applications and resources, Evolutionary Computation,2001. Proceedings of the 2001 Congress on. Publishing, pp.81-86,2001.
[96]Kennedy, J.F., Kennedy, J.,& Eberhart, R.C. Swarm intelligence[M]. Morgan Kaufmann Pub, 2001.
[97]Shi, Y.,& Eberhart, R.C., Empirical study of particle swarm optimization, Evolutionary Computation,1999. CEC 99. Proceedings of the 1999 Congress on. Publishing,1999.
[98]刘军民,&高岳林.混沌粒子群优化算法.计算机应用,28,322-325,2008.
[99]Krink, T., VesterstrOm, J.S.,& Riget, J., Particle swarm optimisation with spatial particle extension, Evolutionary Computation,2002. CEC'02. Proceedings of the 2002 Congress on. Publishing, pp.1474-1479,2002.
[100]吕振肃,&侯志荣.自适应变异的粒子群优化算法.电子学报,32,416-420,2004.
[101]Guo, Z., Dong, Y., Wang, J.,& Lu, H. The Forecasting Procedure for Long-Term Wind Speed in the Zhangye Area. Mathematical Problems in Engineering,20102010.
[102]Introduction of Jiuquan city on Wikipedia; 2013. Available:http://zh.wikipedia.org/wiki/%E9%85%92%E6%B3%89%E5%B8%82. Accessed May 12th,2013.
[103]The Central People's Government of the people's Republic of China; 2013. Available:http://www.gov.cn/jrzg/2009-08/08/content 1386664.htm. Accessed May 12th, 2013.
[104]Ediger, V.S.,& Akar, S. ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy,35,1701-1708,2007.
[105]张得丰.MATLAB小波分析[M].机械工业出版社,2009.
[106]Zhang, Z., Lu, C., Zhang, F., Ren, Y., Yang, K.,& Su, Z. A Novel Method for non-contact measuring diameter parameters of wheelset based on wavelet analysis. Optik-International Journal for Light and Electron Optics,123,433-438,2012.
[107]Dugas, A.F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Igusa, T.,& Rothman, R.E. Influenza Forecasting with Google Flu Trends. PloSone,8, e56176,2013.
[108]Zweighaft, R.M., Fraser, D.W., Hattwick, M., Winkler, W.G., Jordan, W.C., Alter, M., Wolfe, M., Wulff, H.,& Johnson, K.M. Lassa fever:response to an imported case. The New England journal of medicine,297,803,1977.
[109]Centers for Disease Control and Prevention (Overview of Influenza Surveillance in the United States). Available:http://www.cdc.gov/flu/weekly/overview.htm.Accessed March 25th,2013.
[110]Public Health Agency of Canada; Available:http://www.phac-aspc.gc.ca/index-eng.php Accessed March 25th,2013.
[111]Victorian Infectious Diseases Reference Laboratory; Available:http://www.vidrl.org.au/. Accessed March 25th,2013.
[112]National Institute for Communicable Diseases; Available:http://www.nicd.ac.za/. Accessed March 25th,2013.
[113]European Influenza Surveillance Network of the European Centre for Disease Prevention and Control; Available:http://ecdc.europa.eu/en/activities/surveillance/EISN/Pages/index.aspx. Accessed March 25th,2013.
[114]National Institute of Infectious Diseases; Available:http://www.nih.go.jp/niid/.Accessed March 25th,2013.
[115]Signorini, A., Segre, A.M.,& Polgreen, P.M. The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PloS one,6, e19467, 2011.
[116]Culotta, A. Detecting influenza outbreaks by analyzing Twitter messages. arXiv preprint arXiv:1007.47482010.
[117]Google (2013) Google Flu Trends; Available:http://www.google.org/flutrends. Accessed March 25th,2013.
[118]Carneiro, H.A.,& Mylonakis, E. Google trends:a web-based tool for real-time surveillance of disease outbreaks. Clinical infectious diseases,49,1557-1564,2009.
[119]Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S.,& Brilliant, L. Detecting influenza epidemics using search engine query data. Nature 2009,7232,2009.
[120]Ortiz, J.R., Zhou, H., Shay, D.K., Neuzil, K.M., Fowlkes, A.L.,& Goss, C.H. Monitoring influenza activity in the United States:a comparison of traditional surveillance systems with Google Flu Trends. PLoS One,6, e18687,2011.
[121]Cook, S., Conrad, C., Fowlkes, A.L.,& Mohebbi, M.H. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One,6, e23610, 2011.
[122]罗伯特·希斯著,王成,宋炳辉,金瑛译.危机管理[M].中信出版社,2001.