城市空气质量预测模型与数据可视化方法研究
|
摘要
由于工业化、城市化进程的快速发展,城市的生产、消费规模不断扩大,导致城市能源、交通规模持续扩大。我国是发展中国家,能源结构不尽合理,以燃煤作为电力、热力等的主要燃料。因此,以可吸入颗粒物、一氧化碳、二氧化硫、氮氧化物等为主要污染物的大气环境污染问题日趋严重,对资源、环境与人民生活和社会经济带来巨大的冲击,严重威胁着可持续发展的基础。
开展空气质量监测、预测与数据分析与可视化的研究可以全面掌握城市空气污染源的排放数据和各种空气污染物在不同空间区域内的浓度数据,可以对影响城市空气质量的因素有所了解和把握,掌握城市空气质量在时间和空间维的变化发展趋势,对城市规划与建设、污染控制、环境管理、公共事业发展均有重要的理论意义与实用价值。
“十五”规划和“十一五”规划期间,我国各省市都开展了大规模的基于互联网、物联网的基本覆盖城市区域的环境空气质量监测系统和重点空气污染源监测监控系统的建设。对影响城市环境空气质量进行实时监测监控,获取了大量环境空气质量和重点空气污染源排放的基础数据和实时动态监测数据。如何充分利用这些海量数据,来对城市环境空气质量的各种指标的进行科学的评价、对环境空气质量变化趋势进行分析预测,研究城市环境空气质量与各种空气排放源之间的时空相关性等方面的工作有待进一步开展。
在国家863计划项目《大规模动态场景中海量数据的远程可视化技术及系统实现(2008)》、山东省科技攻关项目《城市空气污染应急仿真与可视化研究及系统实现(2008)》和山东省科委《山东省可持续发展科技示范项目-济南市环境空气质量及空气污染源监控、预警技术及监控网络的研究开发(2002))以及济南市科技攻关项目《济南市烟气污染源在线监控系统研究开发与示范(2003)》等科研项目的支持下,本文以山东省济南市的环境监测系统为基础,围绕城市环境空气质量监测监控与预测模型的构建和空气质量数据的可视化等课题,重点开展了基于神经网络的城市环境空气质量预测模型研究、城市环境空气质量预报数据可视化方法的研究、城市空气污染源排放扩散模拟仿真方法的研究、以及城市空气污染源排放监测数据与空气质量的相关性分析等方面的研究。
本文研究了以区域内空气污染源排放监测数据为输入,区域内的环境空气监测站的污染物浓度数据预测值为输出的人工神经网络预测模型的构建方法。提出了一个基于粗糙集理论的BP神经网络预测模型,对指定区域内的环境空气污染源排放原始监测数据进行属性约简、构造人工神经网络隐层的神经元节点、确定节点间的连接权值和网络初始拓扑结构,通过BP算法迭代对数据进行训练,求出网络的各种参数,完成预测模型的构建。提出了一个基于资源分配网络的神经网络预测模型,利用RAN神经网络的距离准则和误差准则,进行隐层节点的动态生成和参数调节,生成能满足误差要求的最小神经网络结构,避免了网络中隐含节点个数和初始网络参数难以选取的缺点,这两个模型,都比经典的BP神经网络有更快的网络的训练速度和更高的预测精度。
本文对城市区域内的环境空气质量预报数据的可视化方法的进行了研究。运用径向基函数对给定区域环境空气质量预报数据中污染物浓度值在时间和空间维度上进行插值的方法,生成了在某一指定空间层次上的空气污染物浓度等值面；采用Marching Cubes算法在城市空间区域维度上进行污染物浓度数据的三维等值面绘制；研究了如何实现城市区域空气质量预报数据中污染物浓度在空间的整体弥撒效果,首先定义了在空间网格中的污染物粒子,并以对空间区域的所有格网中的污染物浓度值数据进行数据预处理、转换,利用光线对污染物的散射效果,实现对空气污染数据动态模拟,绘制出了类似雾霾的视觉效果；研究了基于高斯点源扩散模式的城市空气污染源排放监测数据与城市空气质量监测数据的相关性及可视化方法,在给定的气象数据基础上,计算出一组选定污染源的污染物排放监测数据对城市各环境质量监测站点的污染物浓度值监测数据贡献率,建立了相关性分析矩阵,对两者的相关性进行了分析,并研究了相关性的表征方法。
As the rapid development of industrialization and urbanization, the city's production and consumption have been expanding, it led the urban energy, transportation to grow continue sly. China is a developing country; the energy structure is not reasonable, coal is as the main fuel of the electricity, heat. Thus the atmospheric environment pollution. problems that the repairable particulate matter, carbon monoxide, sulfur dioxide, nitrogen oxides in the air as the main pollutants have become more serious and bring a great social and economic impact on resources, environment, people's lives and socio-economic and that will also threat meanwhile the foundation of the sustainable development.
To develop the air quality monitoring system, forecasting, data analysis and visualization of urban air quality and air pollution sources, will let us master the emissions data of the air pollution sources and the concentration data of a variety of air pollutants at different spatial area.That also can make us understand the factors which impact the urban air quality and grasp the air quality change's trend in time and space dimension. It has very important theoretical significance and practical value in urban planning and construction, pollution control, environmental management and public undertakings.
Many provinces and municipalities in China have carried out the large-scale, urban areas covered ambient air quality monitoring systems and the key sources of air pollution monitoring and control systems by in "Tenth Five-Year Plan" Planning and the "Eleventh Five-Year Plan" period. By using these systems, we can obtain a large number of basis data and the real-time monitoring data of the ambient air qualities and key air pollution sources. How to take advantage of these huge amounts of data, to study and evaluate the urban environment air quality indicators, to analyze and forecast the ambient air quality trends, to study the spatial and temporal correlation between air quality and air emissions of various air pollution sources scientifically will be the important work recently.
With the supported of the National High Technology Research and Development program of China, that the title is "The Massive Amounts of Data in Dynamic Scene Visualization Technology and A Remote System to Achieve" and the Scientific and Technological Project of Shandong province that the title is "Urban Air Pollution Emergency Response Simulation and Visualization and System Implementation" and the project of Shandong Science and Technology that the title is "The Sustainable Development in Shandong Province Science and Technology Demonstration Projects-Jinan Ambient Air Quality and Air Pollution Monitoring, Early Warning and Monitoring Network Technology Research and Development", and the project scientific and technological of Jinan City that the title is "Jinan Smoke Pollution On-line Monitoring Systems Research, Development and Demonstration" and other research projects, this paper focus on the urban environment based on the neural network prediction model of air quality, urban air quality forecasting data visualization methods, and the reverent of the urban air pollution emission monitoring data and the air quality monitoring data based on Jinan's environmental air quality monitoring system and Jinan's air pollution emission monitoring and control systems to build some predictive models based on the neural network,to implement the visualization of the data and to get the correlation of the air quality monitoring data and the air pollution emission monitoring data.
This paper studies the construction method of artificial neural network prediction model with the regional air pollution emissions monitoring data as input, ambient air monitoring stations within the region of the predictive value of pollutant concentration data as output. It proposed a BP neural network prediction model based on a rough set theory, which reduct attributes in a selected area of the original ambient air pollution emission monitoring data, construct of the hidden layer neuron node, of the neural network, determine the initial connection weights between neural nodes and make the initial network topology, obtain the network parameters through training by iterative BP algorithm and complete the construction of predictive models. The paper also proposed a neural network prediction model based on the RAN (Resource Allocation Network), it can meet the minimum requirements of neural network error structure using neural network RAN distance criterion and error criterion, and can generate dynamically the hidden nodes of the neural network and adjust the parameters dynamically. These two models have faster network training speed and higher prediction accuracy than the classical BP neural network.
How to visualize the city's ambient air quality forecasting data has been studied in this paper. We generate the air pollutants concentration isosurface in a specified level of the space using the interpolation method of RBF(Radial Basis Function) for a given air quality forecast regional pollutant's concentrations in the data values in time and space dimensions. Using Marching Cubes algorithm we render the three-dimensional isosurface of pollutants concentration data on the regional dimension in the whole city space. Through the study of regional air pollutant concentration data in spatial area, we establish a pollution concentration data grid cube and how to render the overall diffusion effect for the certain pollutants concentrations data in the specified space area of the urban also be studied in this paper. Firstly, an abstract particle associated with the certain pollutant concentration data of every grid cube in the space is defined, Then pretreated all pollutant concentration data in every grid to form a normalized data set to be rendered, use the abstract particle to construct the corresponding relationship between pollutant concentration of a grid and the particle radius, After that we use the Rayleigh and Mie scattering models to render the all particles in the space and get the Visual effects as haze. The rendering results show that the rendering of the translucent material in spatial region has better visual effect. The paper studies the correlation of the urban air pollution emissions monitoring data and the urban air quality monitoring data and how to visualize it based on Gaussian point source dispersion model. Under the certain meteorological conditions we calculate the contribution rate by selecting some sources of pollutant emissions monitoring data to the air quality monitoring sites of the urban. Established a correlation matrix, analyzed the correlation of two data sets. The paper also studied the visualization technology to visualize the correlation relation of emissions monitoring data of the pollutant sources and that of the air quality monitoring sites.
开展空气质量监测、预测与数据分析与可视化的研究可以全面掌握城市空气污染源的排放数据和各种空气污染物在不同空间区域内的浓度数据,可以对影响城市空气质量的因素有所了解和把握,掌握城市空气质量在时间和空间维的变化发展趋势,对城市规划与建设、污染控制、环境管理、公共事业发展均有重要的理论意义与实用价值。
“十五”规划和“十一五”规划期间,我国各省市都开展了大规模的基于互联网、物联网的基本覆盖城市区域的环境空气质量监测系统和重点空气污染源监测监控系统的建设。对影响城市环境空气质量进行实时监测监控,获取了大量环境空气质量和重点空气污染源排放的基础数据和实时动态监测数据。如何充分利用这些海量数据,来对城市环境空气质量的各种指标的进行科学的评价、对环境空气质量变化趋势进行分析预测,研究城市环境空气质量与各种空气排放源之间的时空相关性等方面的工作有待进一步开展。
在国家863计划项目《大规模动态场景中海量数据的远程可视化技术及系统实现(2008)》、山东省科技攻关项目《城市空气污染应急仿真与可视化研究及系统实现(2008)》和山东省科委《山东省可持续发展科技示范项目-济南市环境空气质量及空气污染源监控、预警技术及监控网络的研究开发(2002))以及济南市科技攻关项目《济南市烟气污染源在线监控系统研究开发与示范(2003)》等科研项目的支持下,本文以山东省济南市的环境监测系统为基础,围绕城市环境空气质量监测监控与预测模型的构建和空气质量数据的可视化等课题,重点开展了基于神经网络的城市环境空气质量预测模型研究、城市环境空气质量预报数据可视化方法的研究、城市空气污染源排放扩散模拟仿真方法的研究、以及城市空气污染源排放监测数据与空气质量的相关性分析等方面的研究。
本文研究了以区域内空气污染源排放监测数据为输入,区域内的环境空气监测站的污染物浓度数据预测值为输出的人工神经网络预测模型的构建方法。提出了一个基于粗糙集理论的BP神经网络预测模型,对指定区域内的环境空气污染源排放原始监测数据进行属性约简、构造人工神经网络隐层的神经元节点、确定节点间的连接权值和网络初始拓扑结构,通过BP算法迭代对数据进行训练,求出网络的各种参数,完成预测模型的构建。提出了一个基于资源分配网络的神经网络预测模型,利用RAN神经网络的距离准则和误差准则,进行隐层节点的动态生成和参数调节,生成能满足误差要求的最小神经网络结构,避免了网络中隐含节点个数和初始网络参数难以选取的缺点,这两个模型,都比经典的BP神经网络有更快的网络的训练速度和更高的预测精度。
本文对城市区域内的环境空气质量预报数据的可视化方法的进行了研究。运用径向基函数对给定区域环境空气质量预报数据中污染物浓度值在时间和空间维度上进行插值的方法,生成了在某一指定空间层次上的空气污染物浓度等值面；采用Marching Cubes算法在城市空间区域维度上进行污染物浓度数据的三维等值面绘制；研究了如何实现城市区域空气质量预报数据中污染物浓度在空间的整体弥撒效果,首先定义了在空间网格中的污染物粒子,并以对空间区域的所有格网中的污染物浓度值数据进行数据预处理、转换,利用光线对污染物的散射效果,实现对空气污染数据动态模拟,绘制出了类似雾霾的视觉效果；研究了基于高斯点源扩散模式的城市空气污染源排放监测数据与城市空气质量监测数据的相关性及可视化方法,在给定的气象数据基础上,计算出一组选定污染源的污染物排放监测数据对城市各环境质量监测站点的污染物浓度值监测数据贡献率,建立了相关性分析矩阵,对两者的相关性进行了分析,并研究了相关性的表征方法。
As the rapid development of industrialization and urbanization, the city's production and consumption have been expanding, it led the urban energy, transportation to grow continue sly. China is a developing country; the energy structure is not reasonable, coal is as the main fuel of the electricity, heat. Thus the atmospheric environment pollution. problems that the repairable particulate matter, carbon monoxide, sulfur dioxide, nitrogen oxides in the air as the main pollutants have become more serious and bring a great social and economic impact on resources, environment, people's lives and socio-economic and that will also threat meanwhile the foundation of the sustainable development.
To develop the air quality monitoring system, forecasting, data analysis and visualization of urban air quality and air pollution sources, will let us master the emissions data of the air pollution sources and the concentration data of a variety of air pollutants at different spatial area.That also can make us understand the factors which impact the urban air quality and grasp the air quality change's trend in time and space dimension. It has very important theoretical significance and practical value in urban planning and construction, pollution control, environmental management and public undertakings.
Many provinces and municipalities in China have carried out the large-scale, urban areas covered ambient air quality monitoring systems and the key sources of air pollution monitoring and control systems by in "Tenth Five-Year Plan" Planning and the "Eleventh Five-Year Plan" period. By using these systems, we can obtain a large number of basis data and the real-time monitoring data of the ambient air qualities and key air pollution sources. How to take advantage of these huge amounts of data, to study and evaluate the urban environment air quality indicators, to analyze and forecast the ambient air quality trends, to study the spatial and temporal correlation between air quality and air emissions of various air pollution sources scientifically will be the important work recently.
With the supported of the National High Technology Research and Development program of China, that the title is "The Massive Amounts of Data in Dynamic Scene Visualization Technology and A Remote System to Achieve" and the Scientific and Technological Project of Shandong province that the title is "Urban Air Pollution Emergency Response Simulation and Visualization and System Implementation" and the project of Shandong Science and Technology that the title is "The Sustainable Development in Shandong Province Science and Technology Demonstration Projects-Jinan Ambient Air Quality and Air Pollution Monitoring, Early Warning and Monitoring Network Technology Research and Development", and the project scientific and technological of Jinan City that the title is "Jinan Smoke Pollution On-line Monitoring Systems Research, Development and Demonstration" and other research projects, this paper focus on the urban environment based on the neural network prediction model of air quality, urban air quality forecasting data visualization methods, and the reverent of the urban air pollution emission monitoring data and the air quality monitoring data based on Jinan's environmental air quality monitoring system and Jinan's air pollution emission monitoring and control systems to build some predictive models based on the neural network,to implement the visualization of the data and to get the correlation of the air quality monitoring data and the air pollution emission monitoring data.
This paper studies the construction method of artificial neural network prediction model with the regional air pollution emissions monitoring data as input, ambient air monitoring stations within the region of the predictive value of pollutant concentration data as output. It proposed a BP neural network prediction model based on a rough set theory, which reduct attributes in a selected area of the original ambient air pollution emission monitoring data, construct of the hidden layer neuron node, of the neural network, determine the initial connection weights between neural nodes and make the initial network topology, obtain the network parameters through training by iterative BP algorithm and complete the construction of predictive models. The paper also proposed a neural network prediction model based on the RAN (Resource Allocation Network), it can meet the minimum requirements of neural network error structure using neural network RAN distance criterion and error criterion, and can generate dynamically the hidden nodes of the neural network and adjust the parameters dynamically. These two models have faster network training speed and higher prediction accuracy than the classical BP neural network.
How to visualize the city's ambient air quality forecasting data has been studied in this paper. We generate the air pollutants concentration isosurface in a specified level of the space using the interpolation method of RBF(Radial Basis Function) for a given air quality forecast regional pollutant's concentrations in the data values in time and space dimensions. Using Marching Cubes algorithm we render the three-dimensional isosurface of pollutants concentration data on the regional dimension in the whole city space. Through the study of regional air pollutant concentration data in spatial area, we establish a pollution concentration data grid cube and how to render the overall diffusion effect for the certain pollutants concentrations data in the specified space area of the urban also be studied in this paper. Firstly, an abstract particle associated with the certain pollutant concentration data of every grid cube in the space is defined, Then pretreated all pollutant concentration data in every grid to form a normalized data set to be rendered, use the abstract particle to construct the corresponding relationship between pollutant concentration of a grid and the particle radius, After that we use the Rayleigh and Mie scattering models to render the all particles in the space and get the Visual effects as haze. The rendering results show that the rendering of the translucent material in spatial region has better visual effect. The paper studies the correlation of the urban air pollution emissions monitoring data and the urban air quality monitoring data and how to visualize it based on Gaussian point source dispersion model. Under the certain meteorological conditions we calculate the contribution rate by selecting some sources of pollutant emissions monitoring data to the air quality monitoring sites of the urban. Established a correlation matrix, analyzed the correlation of two data sets. The paper also studied the visualization technology to visualize the correlation relation of emissions monitoring data of the pollutant sources and that of the air quality monitoring sites.
引文
[1]世界卫生组织(WHO)2006年度报告[R]. http://china-week.com/info/00937.htm.
[2]Grater London Authority. The Mayort's Air Quality Strategy:Progress Report 2005[R]. Greater London Authority,2005. London.
[3]Ipsos MORI. Annual London Survey 2006:'top line'resultS[R].2007. London.
[4]Greater London Authority. The Mayor's Air Quality Strategy[R]. Greater London Authoriy,2002. London.
[5]Douglas, W. Dockery, C. Arden Pope, Xiping Xu, John D. Spengler, James H. Ware, Manha E. Fay, Benjiamin G. Ferris, and Frank. E. Speizer. An Association between Air Pollution and Mortality in Six U. S. Cities[J]. The New England jonnual of medicine,1993(329):1753-1759.
[6]中华人民共和国环境保护部.中国环境状况公报-2009[R].2010.5.31.P24-28.
[7]山东省环境监测监控系统验收鉴定报告[R].山东省环保厅,戈顿三希科技有限公司,2010.5.济南,山东省环保厅.
[8]吕蓓红,曹三忠,张勇.环境空气质量预报准确率研究[J].信阳师范学院学报(自然科学版).2006,19(4)：499-503.
[9]楼建华.永安市环境空气质量预报方法研究及应用[J].海峡科学.2008(5)：41.42.
[10]济南市环境保护科学研究所,中科院大气物理研究所.济南市空气质量数值预报试验研究研究报告[R].2001.6.济南,济南市环境保护科学研究所.
[11]济南市环境保护科学研究所,山东大学.济南市环境空气质量及空气污染源监控、预警技术及监控网络的研究开发技术报告[R].2003.10.济南,济南市环境保护科学研究所.
[12]佟彦超.中国重点城市空气污染预报及其进展[J].中国环境监测.2006,22(2)：P69-71.
[13]蒋维楣,孙鉴宁,曹文俊,蒋瑞宾.空气污染气象学教程(第二版)[M].气象出版社,2004.
[14]N.kh.Arystanbekova. Application of Gaussian plume models for air pollution simulation at instantaneous emissions[J].2004 IMACS. Published by Elsevier B.V.P451-458.
[15]Kostas D. Karatzas, Stamoulis Kaltsatos. Air pollution modeling with the aid of computational intelligence methods in Thessaloniki, Greece[J]. Elsevier B.V. 2007. P 1310-1319.
[16]孙萌阳 巴力登.基于物理模型的烟雾模拟[J].计算机与数字工程.Vol.37 No.7.2009年第7期.P1-3.
[17]谢敏.人工神经网络在城市空气质量预报中的应用研究[D].中山大学硕士学位论文.广东：2009.
[18]刘永,郭怀成.城市人气污染物浓度预测方法研究[J].安全与环境学报,2004,4(4)：60-62.
[19]白晓平,李红,张启明,FRANCESCA Costabile,方栋.人工神经网络在空气污染预报中的研究进展[J].科技导报,2006,24(12)：77-81.
[20]王俭,胡筱敏,郑龙熙等.基于BP模型的大气污染预报方法的研究[J].环境科学研究,2002,15(5)：62-64.
[21]刘罡,李昕,胡非.大气污染浓度的神经网络预报[J].中国环境科学,2000,20(5)：429-431.
[22]阎平凡.多层前馈网络的泛化和学习问题[C].中国神经网络93年学术会议论文集.1993年,上册,68～77.
[23]王向东.基于神经网络的集成电路芯片生产过程建模与优化[D].中国科学院半导体研究所博士学位论文.北京：1999.
[24]祝翠玲,蒋志方等.基于Internet的城市烟气实时监控系统[J].计算机工程.2005,31(10)：200-202.
[25]谭业浩.基于径向基函数插值的环境空气质量数据可视化研究[D].山东大学硕士学位论文.济南：2009.
[26]EL Harbawi, etc. Air Pollution Modelling, Simulation and Computational Methods: A Review[J]. ICERT 2008.
[27]Bosanquet, C. H. and Pearson, J. L., The spread of smoke and gases from chimneys[J], Trans. Faraday Soc.,32:1249,1936
[28]M. Mickiewicz. The fundamentals of air pollution dispersion modeling[M]. Warsaw University of Technology Publishing
[29]Eihchorn J. Validation of microscale pollution dispersion model[J]. The international 19th conference on:Air pollution modeling and its application.1997.
[30]Shim S.G., Carmichael G. M. Analysis of wet deposition in the eastern United States[R]. Atmospheric Environment 1992.
[31]Chang S. Ch. et al. A 3-dimensional Eulerian acid deposition model:physical concept and formulation[J]. Journal of Geophysical Resources.1987.
[32]Juda-Rezler K. Air pollution modeling. The Encyclopedia of Environment Control Technology [J] 1989.
[33]M. Markiewicz. Modeling of the Air Pollution Dispersion[R]. Models and Techniques for Health and Environmental Hazard Assessment and Management.2006.
[33]魏振刚,郭遵强,张琳,邱飞飞.基于高斯模式的大气污染模型的应用[J].中国海洋大学学报.第38卷第2期.2008年3月.P328-330.
[35]张斌才,赵军.大气污染扩散的高斯烟羽模型及其GIS集成研究[J].环境监测管理与技术.第20卷第5期2008年10月.P17-19.
[36]陈世权,崔晓丹,黄严.空气污染预报方法简介[J].黑龙江环境通报.2005,29(1)：47.48.
[37]雷孝恩,等.大气污染数值预报基础和模式[M].北京：中国气象出版社,1998.321.
[38]王志国,王立柱,王锷一.城市空气质量数值预报方法研究[J].上海环境科学.2003年05期
[39]程洁.城市空气污染预报方法及其应用技术研究[J].环境保护,2000.3.P 46.
[40]魏生生,林学范. 北京城区空气污染浓度长期预测[J].气象,Vol.25,No.1,1999.p43-47
[41]National Oceanic and Atmospheric Administration[R]. Air quality, forecast capability. http:www.weather.gov/aq.
[42]朱蓉,徐火海.CAPPS预报方法研究[J].气象.2001,27(6)：10-16.
[43]杜世勇,谭晓哲,田勇,李泰岳,王志国,雷孝恩.济南市空气质量数值预报研究[J].环境科学研究,Vol.15,No.4,2002.P20-23.
[44]阎平凡,张长水.人工神经网络与模拟进化计算[M].清华大学出版社.2000.
[45]罗晓曙.人工神经网络理论与模型.算法与应用[M].广西师范大学出版社.2005.
[46]刘希玉,刘弘.人工神经网络与微粒群优化[M].北京邮电大学出版社.2008.
[47]陈希,李妍,沙文钮等.人工神经网络技术在台风浪预报中的应用[J].海洋科学,2003,27(2)：83-86.
[48]黄海洪,孙崇智,金龙.基于主分量的神经网络水位预报模型应用研究明[J].南京气象学院学报,2005,28(1)：58-63.
[49]刘永,郭怀成.城市人气污染物浓度预测方法研究[J].安全与环境学报,2004,4(4)：60-62
[50]阎平凡.多层前馈网络的泛化和学习问题[c].中国神经网络93年学术会议论文集.1993年,上册,68～77.
[51]王向东.基于神经网络的集成电路芯片生产过程建模与优化[D].中国科学院j仁导体研究所博士学位论文.北京：1999.
[52]Yi,J. and Prybutok, R. (1996)A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area[J]. Environmental pollution 92(3),349-357.
[53]PELLICCIONI A,POLI U. Use of neural network models to forecast atmospheric pollution[J]. Environmental Monitoring and Assessment,2000(65):297-304.
[54]Sang Hyun sohn,etc. Prediction of ozone Formation Based on Neural Network [J]. Envir. Engrg., ASCE,2000,8:688-696.
[55]Jeong-Sock Heo,Dong-Sool Kim.A new method of ozone forecasting using fuzzy expert and neural network systems[J]. Science of the Total Environment,2004,325: 221-237.
[56]D. Karatzas,Stamoulis Kaltsatos. Air pollution modeling with the aid of computational intelligence methods in Thessaloniki, Greece. Elsevier B. V. 2007. P1310-1319.
[57]王俭,胡筱敏,郑龙熙等.基于BP模型的大气污染预报方法的研究[J].环境科学研究,2002,15(5)：62-64.
[58]郎君,苏小红,周秀杰.基于有机灰色神经网络模型的空气污染指数预测[J].哈尔滨工业大学学报,2004,26(12)：1598-1601,1704.
[59]金龙,况雪源,黄海洪等人工神经网络预报模型的过拟合研究[J].气象学报,2004,62(1)：62-69.
[60]刘罡,李昕,胡非.大气污染浓度的神经网络预报[J].中国环境科学,2000,20(5)：429-431.
[61]吴小红,康海燕,任德官.基于神经网络中小城市空气污染指数预估器的设计[J].数学的实践与认识,2005,35(2)：87-91.
[62]李祚泳,彭荔红.基于遗传算法优化的大气质量评价的污染损害指数公式[J].中国环境科学,2001,20(4)：289-291.
[63]裴旭东,黄聪明.对象输入参数优化的人工神经网络方法[J].北京理工大学学报.Vol.20,No.1,2000.01,P61-64.
[64]王恺,杨巨峰,王立,史广顺,王庆人.人工神经网络泛化问题研究综述[J].计算机 应用研究.Vol.25,No.12,2008.12 P3525-2530,3533.
[65]MeComrickB. H., DeFanti. TA., Visualization in Scientific Computing[J]. Computer Graphics, Vol.21, No.6, Nov.1987.
[66]A. E. Kaufman. Volume Visualization[J]. ACM Computing Surveys,1996,3, Vol.28, No.1.
[67]唐泽圣等.三维数据场可视化[M].清华大学出版社,北京.2000.5
[68]黄志澄.给数据以形象给信息以智能--数据可视化技术及其应用展望[C].http://www.visualsky.com/visualtech/viz.htm
[69]曹燕王迎伟.基于AVS/Express平台开发气象模式三维可视化系统的应用研究[R].国家气象中心.2008.8.
[70]中国网.NASA发布全球污染颗粒浓度地图 中国情况最严重[R].http://view. news. qq. com/a/20101006/000002.htm.2010.10.
[71]D.B.Turner, Workbook of Atmospheric Dispersion Estimates[J], U.S. Government Printing Office, Washington, D.C.1970.
[72]柳盛,陈锁忠,王东明.可变风向的空气污染扩散模拟[J].科技信息,2009年第1期.P11-12.
[73]王丽萍,周敏,吴光前.静风条件下烟尘扩散地面浓度模式的研究[J].中国矿业大学学报.第32卷第5期2003年9月.P563-566.
[74]Liangyi Fan, Shanzhen Yi, Zhongyi Li, Bo Yang. Research on toxic Gas Diffusion Simulation in Urban areas based on GIS[C].2009 International Conference on New Trends in Information and Service Science.2009. P166-169.
[75]A. A. Ramadan, M. Al-Sudairawi, S. Alhajraf, A. R. Khan. Total SO2 Emissions from Power Stations and Evaluation of their Impact in Kuwait Using a Gaussian Plume Dispersion Model [J]. American Journal of Environmental Sciences 4(1).2008. P1-12.
[76]N. kh. Arystanbekova. Application of Gaussian plume models for air pollution simulation at instantaneous emissions[M].2004 IMACS. Published by Elsevier B. V.P451-458.
[77]Jos Stam. Stable Fluids[J]. ACM SIGGRAPH 1999.
[78]Ronald Fedkiw, etc. Visual Simulation of Smoke[C]. ACM SIGGRAPH 2001.
[79]Nick Foster and Ronald Fedkiw. Practical Animation of Liquids[C]. ACM SIGGRAPH 2001
[80]Mark Jason Harris. Real-Time Cloud Simulation and Rendering[D]. PhD dissertation.2003
[81]Matthias Moller. Etc. Particle-Based Fluid Simulation for Interactive Applications[C]. Eurographics/SIGGRAPH Symposium on Computer Animation (2003).
[82]William T. Reeves. Particle Systems-A Technique for Modeling a class of Fuzzy Objects[J]. ACM Transactions on Graphics, Vol.2, No.2, April 1983. P 91-108.
[83]M. Harris, A. Lastra. Real-time cloud rendering[C]. Computer Graphics Forum (Eurographics'01 Proc.),20(3):C.76. C.84,2001. P 76-84.
[84]Andrei Stoian. Real-Time Realistic Cloud Rendering and Lighting[C]. http://www.gamedev.net/page/resources/_//183/403/real-time-realistic-cloud-rend ering-and-lighting-r2273.
[85]Kirk Riley, Kavid S. Ebert, Martin Kraus, Jerry Tessendorf and Charles Hansen. Efficient Rendering of Atmospheric Phenomena[J]. Eurographics Symposium on Rendering (2004). P 375-386.
[86]Tamas Umenhoffer, Laszlo Szirmay-Kalos, Gabor Szijarto. Spherical billboards and their application to rendering explosions[C]. In:Proc. of the 2006 Conference on Graphics Interface, P 57-63.
[87]N. Max. Atmospheric Illumination and Shadows[M]. Computer Graphics,1986, 20(4). P 117-124.
[88]黄雷,王章野,王长波,张鑫,彭群生.雨雾天气下光线散射效果的实时绘制[J]. Journal of Software,2006,17(Suppl.).P 126-137.
[89]Kevin Walker. Smoke Signals:Particle Systems for Ambient Information Display[J]. Internet:http://www.exhibitresearch.com/kevin/ioe/smoke.pdf.Last viewed:July,2008.
[90]Bei Wang, Jingliang Peng, and C.-C. Jay Kuo. Cumulus Cloud Synthesis with Similarity Solution and Particle/Voxel Modeling[J]. Springer Berlin/Heidelberg. Volume 5358/2008.2008.
[91]Col inn Ware. Information Visualization, Second Edition:Perception for Design [M]. Morgan Kaufman Publishers.2004.
[92]董士海.人机交互的进展及面临的挑战[J].计算机辅助设计与图形学学报.16(1)：1-13,2004
[93]Cooper G R J, "Analyzing potential field data using visibility" [J]. Computer & Geoscience,31 (7),2005. P 877-881.
[94]Jean-Daniel, F., et al., The Value of Information Visualization, in Information Visualization:Human-Centered Issues and Perspectives[J].2008, Springer-Verlag. p.1-18..
[95]An introduction to visualization by joel laumans[J]. http://piksels.com/ wp-content/uploads/2009/01/visualizingdata.pdf.
[96]Matthew Partridge, Rafael Calvo[J]. Fast Dimensionality Reduction and Simple PCA. Intelligent Data Analysis,2(3).1997. P 292-298
[97]Kan Liu, Ping Liu, Dawei Jin. Stimulation Spectrum Based High-dimensional Data Visualization[J]. Proceedings of the Information Visualization (IV'06). 2006. P721-724.
[98]Johan A. K. Suykens. Data Visualization and Dimensionality Reduction Using Kernel Maps with a Reference Point [J]. IEEE Tranctions On Neural Networks, VOL.19, NO.9, September.2008. P1501-1517.
[99]Jimmy Johansson, Patric Ljung, Mikael Jern, Mattew Cooper. Revealing Structure within Clustered Parallel Coordinates Displays[C]. In INFOVIS'05.2005.P 125-132.
[100]Elena Fanea, Sheelagh Carpendale, Tobias lsenberg. An Interactive 3D Integration of Parallel Coordinates and Star Glyphs[C]. IEEE Symposium on Information Visualization 2005. Pages.149-156.
[101]Ying-Huev Fua, Matthew 0. Ward, Elke A. Rundensteiner. Hierarchical Parallel Coordinates for Exploration of Large Datasets[C]. In Proc. of IEEE Visualization'99,1999. P 43-50.
[102]Helwig Hauser, Florian Ledermann, Helmut Doleisch. Angular Brushing of Extended Parallel Coordinates[C]. Proceedings of InfoVis'2002, Boston, Massachusetts, USA, Oct.2002. P 28-29.
[103]Novotny M. Visually effective information visualization of large data[C]. In Proc. of the 8th Central European Seminar on Computer Graphics (2004).
[104]Christian Tominski, James Abello, Heidrun Schumann. Axes-Based Visualizations with Radial Layouts[C].2004 ACM Symposium on Applied Computing.2004. P1242-1247.
[105]Chao-Kuei Hung Alfred Inselberg. Visualizing Multidimensional Relations with Parallel Coordinates[C].2006 International Conference on Information Technology: Research and Education.2006. P 261-265.
[106]Liang Zhu, Bing-fang WU, Yue-min Zhou, Xin-hui Ma, Lei-dong Yang. Research on Eco-environmental Data Visualization for Three Gorges Project[C].2009 International Symposium on Information Engineering and Electronic Commerce.2009. P 590-593.
[107]Matlab神经网络与应用[M]-董长虹.北京：国防工业出版,2005.
[108]李义宝,张学勇,马建国,汪力君.基于BP神经网络的改进算法研究[J].合肥工业大学学报(自然科学版),2005,28(6)：97-100.
[109]Pawlak Z, Busse J G, etal. Rough sets[J]. Communications of the ACM.1995, Vol.38:89-95.
[110]苗夺谦,李道国.粗糙集理论、算法与应用(中国计算机学会学术著作丛书)[M].清华大学出版社.北京2008.
[111]张文秀.粗糙集理论与方法[M].北京科学出版社.2001.
[112]李仁璞,王正欧.基于粗集理论和神经网络结合的数据挖掘新方法[J].情报学报.2002年,第21卷(第6期)：674-679.
[113]李观松.城市环境空气质量数据挖掘与可视化的研究[D].山东大学,硕士学位论文.2007.
[114]Zhifang Jiang, Xiangxu Meng, Chenglei Yang, Guansong Li. A BP Neural Network Prediction Model of the Urban Air Quality Based on Rough Set[C]. The 4rd International Conference on Natural Computation (ICNC' 08),2008.10, VOL1, NO1,P 362-370
[115]Platt J.A resource-allocating network for function interpolation[J]. neural Computing, Vol.3, No.2, pp:212-225, Jun.1991.
[116]Chen S, Cowan C F N and Grant P M. Orthogonal Least Squares Learning Algorithms for Radial Basis Function Networks[J]. IEEE Trans. Neural Networks,1991,2(2):302-309.
[117]Du xiaoliang, Jiang Zhifang, Meng Xiangxu, Liu Shucheng, Li Shenfang. An Air Quality Forecast Model Based on the BP Neural Network of the Samples Self-Organization Clustering[C].The 6th International Conference on Natural Computation (ICNC 2010), pp:1523-1527,2010.
[118]杜晓亮.人工神经网络在空气质量预测中的应用研究[D].山东大学硕士学位论文.2009年.
[119]Jiang Zhifang, Zhang Shanxiang, Zhang Zhenghai Meng Xiangxu. An Air Quality Predicting Model Based on the Resource Allocation Network[C].2011 International Conference on Computational Intelligence and Software Engineering (CiSE 2011). Wuhan, China.
[120]谭业浩.基于径向基函数插值的环境空气质量数据可视化研究[D].山东大学硕士学位论文.2009.7.
[121]谭业浩；蒋志方；杜晓亮；孟祥旭.紧支径向基函数插值实现多维数据可视化[J].计算机工程与应用,2010,Vol.46,No 9,P220-223.
[122]王靖波,潘懋,张绪定.基于Kriging方法的空间散乱点插值[J].计算机辅助设计与图形学学报,1999,11(6)：525-529.
[123]靳国栋,刘衍聪,牛文杰.距离加权反比插值法和克里金插值法的比较[J].长春工业大学学报(自然科学版),2003,(03).
[124]史利民,王仁宏.几种基于散乱数据拟合的局部插值方法[J].数学研究与评论,2006,(02).
[125]陈荣华.径向基函数拟插值理论及其在微分方程数值解中的应用[D].复旦大学.2005年.
[126]Mathron M. Spline and Kriging their formal equivalence[J]. Les Cahiers du Centre de Morphologie Mathematique de Fontainebleau,1980.
[127]苏姝,林爱文,刘庆华.普通Kriging法在空间内插中的运用[J].江南大学学报(自然科学版),2004,(01).
[127]吴宗敏.函数的径向基表示[J].数学进展,1998,3(27)：202-208.
[128]A. Sommariva. M. Vianello. Numerical Cubature on Scattered Data by Radial Basis Functions[J]. Computing archive.2006,1:295-310.
[129]颜慧敏.空间插值技术的开发与实现.[M].西南石油学院.2006.
[130]R.L.Hardy, Multiquadric equations of topography and other irregular surfaces, [J] Geophysics Res.,1971,176:1905-1915.
[131]M. D. Buhmann. Radial basis functions[M]. in:Acta Numerica Vol.9,2000, Cambridge:Cambridge University Press,2000:1-38.
[132]孔亮,高学军,王燕昌.基于紧支径向基函数的点插值无网格方法[J].岩土力学,2004,(S2).
[133]宋康祖.紧支函数无网格方法研究[D].北京：清华大学.工程力学系,2000.
[134]高学军,朱一丁.基于紧支径向基函数的局部径向点插值方法[J].安徽建筑工业学院学报.2007,Vol.15,No.3 P86-90.
[135]Morse B S. Piecewise polynomial positive definite and compactly supported radial functions of minimal degree[J]. A ICM,1995 (4):P389-396
[136]Lorensen WE, Cline HE. Marching cubes:a high-resolution 3D surface construction algorithm[J]. Computer Graph 1987;21(4):P163-169.
[137]Miguet S, Nicod J-M. A load-balanced parallel implementation of the marching-cube algorithm[J]. In:Proceedings of high performance computing symposium'95, Montreal,1995. p. P229-239.
[138]van Gelder A, Wilhelms J. Topological considerations in isosurface generation[J]. ACM Transactions on Graphics 1994;13(4):P337-375.
[139]Zhou C, Shu R, Kankanhalli M. Handing small features in isosurface generation using Marching Cubes[J]. Comput Graph 1994;18:P845-848.
[140]Timothy S. Newman, Hong Yi. A survey of the marching cubes algorithm[J]. Computers & Graphics 30 (2006) P854-879.
[141]王香菊.图像去噪方法及应用.科技情报开发与经济[J].2007年第17卷第27期.P213-215.
[142]PELLICCIONI A, POLI U. Use of neural net models to forecast atmospheric pollution[J]. Environmental Monitoring and Assessment,2000(65):P297-304.
[143]王长波,王章野,曾运,彭群生.考虑大气折射的天空场景真实感绘制[J].计算机学报Chinese Journal Of Computers. Vol.28(6),June 2005. P 539-549.
[144]Jackel D.,Walter B..Modeling and rendering of the atmosphere using Mie-scattering[J]. Computer Graphics Forum,1997,16(4). P 201-210.
[145]M. Harris, A. Lastra. Real-time cloud rendering[J]. Computer Graphics Forum (Eurographics'01 Proc.),20(3):C.76. C.84,2001. P 76-84.
[146]Kirk Riley, Kavid S. Ebert, Martin Kraus, Jerry Tessendorf and Charles Hansen. Efficient Rendering of Atmospheric Phenomena[C]. Eurographics Symposium on Rendering (2004). P375-386.
[147]李苗苗.区域空气污染扩散数据可视化技术的研究[D].山东大学硕士学位论文.2011.6.
[148]孙娟,束炯,邱报,段玉森,王强.工业点源大气污染扩散空间信息系统[J].环境污染与防治.第27卷第7期.2005.10.P519-522.
[149]黄竞伟,康立山,陈毓屏.一个新的无向图画图算法[J].软件学报,2000,11(1):138-142. http://ww.jos.org.cn/1000-9825/11/138.htm
[150]任磊,王威信,周明骏,腾东兴,马翠霞,戴国忠,王宏安[J].一种模型驱动的交互式信息可视化开发方法 .软件学报,2008,19(8)：1947-1964.http://www.jos.org.cn/1000-9825/19/1947.htm[doi:10.3724/SP.J.1001.2008.01947]
[2]Grater London Authority. The Mayort's Air Quality Strategy:Progress Report 2005[R]. Greater London Authority,2005. London.
[3]Ipsos MORI. Annual London Survey 2006:'top line'resultS[R].2007. London.
[4]Greater London Authority. The Mayor's Air Quality Strategy[R]. Greater London Authoriy,2002. London.
[5]Douglas, W. Dockery, C. Arden Pope, Xiping Xu, John D. Spengler, James H. Ware, Manha E. Fay, Benjiamin G. Ferris, and Frank. E. Speizer. An Association between Air Pollution and Mortality in Six U. S. Cities[J]. The New England jonnual of medicine,1993(329):1753-1759.
[6]中华人民共和国环境保护部.中国环境状况公报-2009[R].2010.5.31.P24-28.
[7]山东省环境监测监控系统验收鉴定报告[R].山东省环保厅,戈顿三希科技有限公司,2010.5.济南,山东省环保厅.
[8]吕蓓红,曹三忠,张勇.环境空气质量预报准确率研究[J].信阳师范学院学报(自然科学版).2006,19(4)：499-503.
[9]楼建华.永安市环境空气质量预报方法研究及应用[J].海峡科学.2008(5)：41.42.
[10]济南市环境保护科学研究所,中科院大气物理研究所.济南市空气质量数值预报试验研究研究报告[R].2001.6.济南,济南市环境保护科学研究所.
[11]济南市环境保护科学研究所,山东大学.济南市环境空气质量及空气污染源监控、预警技术及监控网络的研究开发技术报告[R].2003.10.济南,济南市环境保护科学研究所.
[12]佟彦超.中国重点城市空气污染预报及其进展[J].中国环境监测.2006,22(2)：P69-71.
[13]蒋维楣,孙鉴宁,曹文俊,蒋瑞宾.空气污染气象学教程(第二版)[M].气象出版社,2004.
[14]N.kh.Arystanbekova. Application of Gaussian plume models for air pollution simulation at instantaneous emissions[J].2004 IMACS. Published by Elsevier B.V.P451-458.
[15]Kostas D. Karatzas, Stamoulis Kaltsatos. Air pollution modeling with the aid of computational intelligence methods in Thessaloniki, Greece[J]. Elsevier B.V. 2007. P 1310-1319.
[16]孙萌阳 巴力登.基于物理模型的烟雾模拟[J].计算机与数字工程.Vol.37 No.7.2009年第7期.P1-3.
[17]谢敏.人工神经网络在城市空气质量预报中的应用研究[D].中山大学硕士学位论文.广东：2009.
[18]刘永,郭怀成.城市人气污染物浓度预测方法研究[J].安全与环境学报,2004,4(4)：60-62.
[19]白晓平,李红,张启明,FRANCESCA Costabile,方栋.人工神经网络在空气污染预报中的研究进展[J].科技导报,2006,24(12)：77-81.
[20]王俭,胡筱敏,郑龙熙等.基于BP模型的大气污染预报方法的研究[J].环境科学研究,2002,15(5)：62-64.
[21]刘罡,李昕,胡非.大气污染浓度的神经网络预报[J].中国环境科学,2000,20(5)：429-431.
[22]阎平凡.多层前馈网络的泛化和学习问题[C].中国神经网络93年学术会议论文集.1993年,上册,68～77.
[23]王向东.基于神经网络的集成电路芯片生产过程建模与优化[D].中国科学院半导体研究所博士学位论文.北京：1999.
[24]祝翠玲,蒋志方等.基于Internet的城市烟气实时监控系统[J].计算机工程.2005,31(10)：200-202.
[25]谭业浩.基于径向基函数插值的环境空气质量数据可视化研究[D].山东大学硕士学位论文.济南：2009.
[26]EL Harbawi, etc. Air Pollution Modelling, Simulation and Computational Methods: A Review[J]. ICERT 2008.
[27]Bosanquet, C. H. and Pearson, J. L., The spread of smoke and gases from chimneys[J], Trans. Faraday Soc.,32:1249,1936
[28]M. Mickiewicz. The fundamentals of air pollution dispersion modeling[M]. Warsaw University of Technology Publishing
[29]Eihchorn J. Validation of microscale pollution dispersion model[J]. The international 19th conference on:Air pollution modeling and its application.1997.
[30]Shim S.G., Carmichael G. M. Analysis of wet deposition in the eastern United States[R]. Atmospheric Environment 1992.
[31]Chang S. Ch. et al. A 3-dimensional Eulerian acid deposition model:physical concept and formulation[J]. Journal of Geophysical Resources.1987.
[32]Juda-Rezler K. Air pollution modeling. The Encyclopedia of Environment Control Technology [J] 1989.
[33]M. Markiewicz. Modeling of the Air Pollution Dispersion[R]. Models and Techniques for Health and Environmental Hazard Assessment and Management.2006.
[33]魏振刚,郭遵强,张琳,邱飞飞.基于高斯模式的大气污染模型的应用[J].中国海洋大学学报.第38卷第2期.2008年3月.P328-330.
[35]张斌才,赵军.大气污染扩散的高斯烟羽模型及其GIS集成研究[J].环境监测管理与技术.第20卷第5期2008年10月.P17-19.
[36]陈世权,崔晓丹,黄严.空气污染预报方法简介[J].黑龙江环境通报.2005,29(1)：47.48.
[37]雷孝恩,等.大气污染数值预报基础和模式[M].北京：中国气象出版社,1998.321.
[38]王志国,王立柱,王锷一.城市空气质量数值预报方法研究[J].上海环境科学.2003年05期
[39]程洁.城市空气污染预报方法及其应用技术研究[J].环境保护,2000.3.P 46.
[40]魏生生,林学范. 北京城区空气污染浓度长期预测[J].气象,Vol.25,No.1,1999.p43-47
[41]National Oceanic and Atmospheric Administration[R]. Air quality, forecast capability. http:www.weather.gov/aq.
[42]朱蓉,徐火海.CAPPS预报方法研究[J].气象.2001,27(6)：10-16.
[43]杜世勇,谭晓哲,田勇,李泰岳,王志国,雷孝恩.济南市空气质量数值预报研究[J].环境科学研究,Vol.15,No.4,2002.P20-23.
[44]阎平凡,张长水.人工神经网络与模拟进化计算[M].清华大学出版社.2000.
[45]罗晓曙.人工神经网络理论与模型.算法与应用[M].广西师范大学出版社.2005.
[46]刘希玉,刘弘.人工神经网络与微粒群优化[M].北京邮电大学出版社.2008.
[47]陈希,李妍,沙文钮等.人工神经网络技术在台风浪预报中的应用[J].海洋科学,2003,27(2)：83-86.
[48]黄海洪,孙崇智,金龙.基于主分量的神经网络水位预报模型应用研究明[J].南京气象学院学报,2005,28(1)：58-63.
[49]刘永,郭怀成.城市人气污染物浓度预测方法研究[J].安全与环境学报,2004,4(4)：60-62
[50]阎平凡.多层前馈网络的泛化和学习问题[c].中国神经网络93年学术会议论文集.1993年,上册,68～77.
[51]王向东.基于神经网络的集成电路芯片生产过程建模与优化[D].中国科学院j仁导体研究所博士学位论文.北京：1999.
[52]Yi,J. and Prybutok, R. (1996)A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area[J]. Environmental pollution 92(3),349-357.
[53]PELLICCIONI A,POLI U. Use of neural network models to forecast atmospheric pollution[J]. Environmental Monitoring and Assessment,2000(65):297-304.
[54]Sang Hyun sohn,etc. Prediction of ozone Formation Based on Neural Network [J]. Envir. Engrg., ASCE,2000,8:688-696.
[55]Jeong-Sock Heo,Dong-Sool Kim.A new method of ozone forecasting using fuzzy expert and neural network systems[J]. Science of the Total Environment,2004,325: 221-237.
[56]D. Karatzas,Stamoulis Kaltsatos. Air pollution modeling with the aid of computational intelligence methods in Thessaloniki, Greece. Elsevier B. V. 2007. P1310-1319.
[57]王俭,胡筱敏,郑龙熙等.基于BP模型的大气污染预报方法的研究[J].环境科学研究,2002,15(5)：62-64.
[58]郎君,苏小红,周秀杰.基于有机灰色神经网络模型的空气污染指数预测[J].哈尔滨工业大学学报,2004,26(12)：1598-1601,1704.
[59]金龙,况雪源,黄海洪等人工神经网络预报模型的过拟合研究[J].气象学报,2004,62(1)：62-69.
[60]刘罡,李昕,胡非.大气污染浓度的神经网络预报[J].中国环境科学,2000,20(5)：429-431.
[61]吴小红,康海燕,任德官.基于神经网络中小城市空气污染指数预估器的设计[J].数学的实践与认识,2005,35(2)：87-91.
[62]李祚泳,彭荔红.基于遗传算法优化的大气质量评价的污染损害指数公式[J].中国环境科学,2001,20(4)：289-291.
[63]裴旭东,黄聪明.对象输入参数优化的人工神经网络方法[J].北京理工大学学报.Vol.20,No.1,2000.01,P61-64.
[64]王恺,杨巨峰,王立,史广顺,王庆人.人工神经网络泛化问题研究综述[J].计算机 应用研究.Vol.25,No.12,2008.12 P3525-2530,3533.
[65]MeComrickB. H., DeFanti. TA., Visualization in Scientific Computing[J]. Computer Graphics, Vol.21, No.6, Nov.1987.
[66]A. E. Kaufman. Volume Visualization[J]. ACM Computing Surveys,1996,3, Vol.28, No.1.
[67]唐泽圣等.三维数据场可视化[M].清华大学出版社,北京.2000.5
[68]黄志澄.给数据以形象给信息以智能--数据可视化技术及其应用展望[C].http://www.visualsky.com/visualtech/viz.htm
[69]曹燕王迎伟.基于AVS/Express平台开发气象模式三维可视化系统的应用研究[R].国家气象中心.2008.8.
[70]中国网.NASA发布全球污染颗粒浓度地图 中国情况最严重[R].http://view. news. qq. com/a/20101006/000002.htm.2010.10.
[71]D.B.Turner, Workbook of Atmospheric Dispersion Estimates[J], U.S. Government Printing Office, Washington, D.C.1970.
[72]柳盛,陈锁忠,王东明.可变风向的空气污染扩散模拟[J].科技信息,2009年第1期.P11-12.
[73]王丽萍,周敏,吴光前.静风条件下烟尘扩散地面浓度模式的研究[J].中国矿业大学学报.第32卷第5期2003年9月.P563-566.
[74]Liangyi Fan, Shanzhen Yi, Zhongyi Li, Bo Yang. Research on toxic Gas Diffusion Simulation in Urban areas based on GIS[C].2009 International Conference on New Trends in Information and Service Science.2009. P166-169.
[75]A. A. Ramadan, M. Al-Sudairawi, S. Alhajraf, A. R. Khan. Total SO2 Emissions from Power Stations and Evaluation of their Impact in Kuwait Using a Gaussian Plume Dispersion Model [J]. American Journal of Environmental Sciences 4(1).2008. P1-12.
[76]N. kh. Arystanbekova. Application of Gaussian plume models for air pollution simulation at instantaneous emissions[M].2004 IMACS. Published by Elsevier B. V.P451-458.
[77]Jos Stam. Stable Fluids[J]. ACM SIGGRAPH 1999.
[78]Ronald Fedkiw, etc. Visual Simulation of Smoke[C]. ACM SIGGRAPH 2001.
[79]Nick Foster and Ronald Fedkiw. Practical Animation of Liquids[C]. ACM SIGGRAPH 2001
[80]Mark Jason Harris. Real-Time Cloud Simulation and Rendering[D]. PhD dissertation.2003
[81]Matthias Moller. Etc. Particle-Based Fluid Simulation for Interactive Applications[C]. Eurographics/SIGGRAPH Symposium on Computer Animation (2003).
[82]William T. Reeves. Particle Systems-A Technique for Modeling a class of Fuzzy Objects[J]. ACM Transactions on Graphics, Vol.2, No.2, April 1983. P 91-108.
[83]M. Harris, A. Lastra. Real-time cloud rendering[C]. Computer Graphics Forum (Eurographics'01 Proc.),20(3):C.76. C.84,2001. P 76-84.
[84]Andrei Stoian. Real-Time Realistic Cloud Rendering and Lighting[C]. http://www.gamedev.net/page/resources/_//183/403/real-time-realistic-cloud-rend ering-and-lighting-r2273.
[85]Kirk Riley, Kavid S. Ebert, Martin Kraus, Jerry Tessendorf and Charles Hansen. Efficient Rendering of Atmospheric Phenomena[J]. Eurographics Symposium on Rendering (2004). P 375-386.
[86]Tamas Umenhoffer, Laszlo Szirmay-Kalos, Gabor Szijarto. Spherical billboards and their application to rendering explosions[C]. In:Proc. of the 2006 Conference on Graphics Interface, P 57-63.
[87]N. Max. Atmospheric Illumination and Shadows[M]. Computer Graphics,1986, 20(4). P 117-124.
[88]黄雷,王章野,王长波,张鑫,彭群生.雨雾天气下光线散射效果的实时绘制[J]. Journal of Software,2006,17(Suppl.).P 126-137.
[89]Kevin Walker. Smoke Signals:Particle Systems for Ambient Information Display[J]. Internet:http://www.exhibitresearch.com/kevin/ioe/smoke.pdf.Last viewed:July,2008.
[90]Bei Wang, Jingliang Peng, and C.-C. Jay Kuo. Cumulus Cloud Synthesis with Similarity Solution and Particle/Voxel Modeling[J]. Springer Berlin/Heidelberg. Volume 5358/2008.2008.
[91]Col inn Ware. Information Visualization, Second Edition:Perception for Design [M]. Morgan Kaufman Publishers.2004.
[92]董士海.人机交互的进展及面临的挑战[J].计算机辅助设计与图形学学报.16(1)：1-13,2004
[93]Cooper G R J, "Analyzing potential field data using visibility" [J]. Computer & Geoscience,31 (7),2005. P 877-881.
[94]Jean-Daniel, F., et al., The Value of Information Visualization, in Information Visualization:Human-Centered Issues and Perspectives[J].2008, Springer-Verlag. p.1-18..
[95]An introduction to visualization by joel laumans[J]. http://piksels.com/ wp-content/uploads/2009/01/visualizingdata.pdf.
[96]Matthew Partridge, Rafael Calvo[J]. Fast Dimensionality Reduction and Simple PCA. Intelligent Data Analysis,2(3).1997. P 292-298
[97]Kan Liu, Ping Liu, Dawei Jin. Stimulation Spectrum Based High-dimensional Data Visualization[J]. Proceedings of the Information Visualization (IV'06). 2006. P721-724.
[98]Johan A. K. Suykens. Data Visualization and Dimensionality Reduction Using Kernel Maps with a Reference Point [J]. IEEE Tranctions On Neural Networks, VOL.19, NO.9, September.2008. P1501-1517.
[99]Jimmy Johansson, Patric Ljung, Mikael Jern, Mattew Cooper. Revealing Structure within Clustered Parallel Coordinates Displays[C]. In INFOVIS'05.2005.P 125-132.
[100]Elena Fanea, Sheelagh Carpendale, Tobias lsenberg. An Interactive 3D Integration of Parallel Coordinates and Star Glyphs[C]. IEEE Symposium on Information Visualization 2005. Pages.149-156.
[101]Ying-Huev Fua, Matthew 0. Ward, Elke A. Rundensteiner. Hierarchical Parallel Coordinates for Exploration of Large Datasets[C]. In Proc. of IEEE Visualization'99,1999. P 43-50.
[102]Helwig Hauser, Florian Ledermann, Helmut Doleisch. Angular Brushing of Extended Parallel Coordinates[C]. Proceedings of InfoVis'2002, Boston, Massachusetts, USA, Oct.2002. P 28-29.
[103]Novotny M. Visually effective information visualization of large data[C]. In Proc. of the 8th Central European Seminar on Computer Graphics (2004).
[104]Christian Tominski, James Abello, Heidrun Schumann. Axes-Based Visualizations with Radial Layouts[C].2004 ACM Symposium on Applied Computing.2004. P1242-1247.
[105]Chao-Kuei Hung Alfred Inselberg. Visualizing Multidimensional Relations with Parallel Coordinates[C].2006 International Conference on Information Technology: Research and Education.2006. P 261-265.
[106]Liang Zhu, Bing-fang WU, Yue-min Zhou, Xin-hui Ma, Lei-dong Yang. Research on Eco-environmental Data Visualization for Three Gorges Project[C].2009 International Symposium on Information Engineering and Electronic Commerce.2009. P 590-593.
[107]Matlab神经网络与应用[M]-董长虹.北京：国防工业出版,2005.
[108]李义宝,张学勇,马建国,汪力君.基于BP神经网络的改进算法研究[J].合肥工业大学学报(自然科学版),2005,28(6)：97-100.
[109]Pawlak Z, Busse J G, etal. Rough sets[J]. Communications of the ACM.1995, Vol.38:89-95.
[110]苗夺谦,李道国.粗糙集理论、算法与应用(中国计算机学会学术著作丛书)[M].清华大学出版社.北京2008.
[111]张文秀.粗糙集理论与方法[M].北京科学出版社.2001.
[112]李仁璞,王正欧.基于粗集理论和神经网络结合的数据挖掘新方法[J].情报学报.2002年,第21卷(第6期)：674-679.
[113]李观松.城市环境空气质量数据挖掘与可视化的研究[D].山东大学,硕士学位论文.2007.
[114]Zhifang Jiang, Xiangxu Meng, Chenglei Yang, Guansong Li. A BP Neural Network Prediction Model of the Urban Air Quality Based on Rough Set[C]. The 4rd International Conference on Natural Computation (ICNC' 08),2008.10, VOL1, NO1,P 362-370
[115]Platt J.A resource-allocating network for function interpolation[J]. neural Computing, Vol.3, No.2, pp:212-225, Jun.1991.
[116]Chen S, Cowan C F N and Grant P M. Orthogonal Least Squares Learning Algorithms for Radial Basis Function Networks[J]. IEEE Trans. Neural Networks,1991,2(2):302-309.
[117]Du xiaoliang, Jiang Zhifang, Meng Xiangxu, Liu Shucheng, Li Shenfang. An Air Quality Forecast Model Based on the BP Neural Network of the Samples Self-Organization Clustering[C].The 6th International Conference on Natural Computation (ICNC 2010), pp:1523-1527,2010.
[118]杜晓亮.人工神经网络在空气质量预测中的应用研究[D].山东大学硕士学位论文.2009年.
[119]Jiang Zhifang, Zhang Shanxiang, Zhang Zhenghai Meng Xiangxu. An Air Quality Predicting Model Based on the Resource Allocation Network[C].2011 International Conference on Computational Intelligence and Software Engineering (CiSE 2011). Wuhan, China.
[120]谭业浩.基于径向基函数插值的环境空气质量数据可视化研究[D].山东大学硕士学位论文.2009.7.
[121]谭业浩；蒋志方；杜晓亮；孟祥旭.紧支径向基函数插值实现多维数据可视化[J].计算机工程与应用,2010,Vol.46,No 9,P220-223.
[122]王靖波,潘懋,张绪定.基于Kriging方法的空间散乱点插值[J].计算机辅助设计与图形学学报,1999,11(6)：525-529.
[123]靳国栋,刘衍聪,牛文杰.距离加权反比插值法和克里金插值法的比较[J].长春工业大学学报(自然科学版),2003,(03).
[124]史利民,王仁宏.几种基于散乱数据拟合的局部插值方法[J].数学研究与评论,2006,(02).
[125]陈荣华.径向基函数拟插值理论及其在微分方程数值解中的应用[D].复旦大学.2005年.
[126]Mathron M. Spline and Kriging their formal equivalence[J]. Les Cahiers du Centre de Morphologie Mathematique de Fontainebleau,1980.
[127]苏姝,林爱文,刘庆华.普通Kriging法在空间内插中的运用[J].江南大学学报(自然科学版),2004,(01).
[127]吴宗敏.函数的径向基表示[J].数学进展,1998,3(27)：202-208.
[128]A. Sommariva. M. Vianello. Numerical Cubature on Scattered Data by Radial Basis Functions[J]. Computing archive.2006,1:295-310.
[129]颜慧敏.空间插值技术的开发与实现.[M].西南石油学院.2006.
[130]R.L.Hardy, Multiquadric equations of topography and other irregular surfaces, [J] Geophysics Res.,1971,176:1905-1915.
[131]M. D. Buhmann. Radial basis functions[M]. in:Acta Numerica Vol.9,2000, Cambridge:Cambridge University Press,2000:1-38.
[132]孔亮,高学军,王燕昌.基于紧支径向基函数的点插值无网格方法[J].岩土力学,2004,(S2).
[133]宋康祖.紧支函数无网格方法研究[D].北京：清华大学.工程力学系,2000.
[134]高学军,朱一丁.基于紧支径向基函数的局部径向点插值方法[J].安徽建筑工业学院学报.2007,Vol.15,No.3 P86-90.
[135]Morse B S. Piecewise polynomial positive definite and compactly supported radial functions of minimal degree[J]. A ICM,1995 (4):P389-396
[136]Lorensen WE, Cline HE. Marching cubes:a high-resolution 3D surface construction algorithm[J]. Computer Graph 1987;21(4):P163-169.
[137]Miguet S, Nicod J-M. A load-balanced parallel implementation of the marching-cube algorithm[J]. In:Proceedings of high performance computing symposium'95, Montreal,1995. p. P229-239.
[138]van Gelder A, Wilhelms J. Topological considerations in isosurface generation[J]. ACM Transactions on Graphics 1994;13(4):P337-375.
[139]Zhou C, Shu R, Kankanhalli M. Handing small features in isosurface generation using Marching Cubes[J]. Comput Graph 1994;18:P845-848.
[140]Timothy S. Newman, Hong Yi. A survey of the marching cubes algorithm[J]. Computers & Graphics 30 (2006) P854-879.
[141]王香菊.图像去噪方法及应用.科技情报开发与经济[J].2007年第17卷第27期.P213-215.
[142]PELLICCIONI A, POLI U. Use of neural net models to forecast atmospheric pollution[J]. Environmental Monitoring and Assessment,2000(65):P297-304.
[143]王长波,王章野,曾运,彭群生.考虑大气折射的天空场景真实感绘制[J].计算机学报Chinese Journal Of Computers. Vol.28(6),June 2005. P 539-549.
[144]Jackel D.,Walter B..Modeling and rendering of the atmosphere using Mie-scattering[J]. Computer Graphics Forum,1997,16(4). P 201-210.
[145]M. Harris, A. Lastra. Real-time cloud rendering[J]. Computer Graphics Forum (Eurographics'01 Proc.),20(3):C.76. C.84,2001. P 76-84.
[146]Kirk Riley, Kavid S. Ebert, Martin Kraus, Jerry Tessendorf and Charles Hansen. Efficient Rendering of Atmospheric Phenomena[C]. Eurographics Symposium on Rendering (2004). P375-386.
[147]李苗苗.区域空气污染扩散数据可视化技术的研究[D].山东大学硕士学位论文.2011.6.
[148]孙娟,束炯,邱报,段玉森,王强.工业点源大气污染扩散空间信息系统[J].环境污染与防治.第27卷第7期.2005.10.P519-522.
[149]黄竞伟,康立山,陈毓屏.一个新的无向图画图算法[J].软件学报,2000,11(1):138-142. http://ww.jos.org.cn/1000-9825/11/138.htm
[150]任磊,王威信,周明骏,腾东兴,马翠霞,戴国忠,王宏安[J].一种模型驱动的交互式信息可视化开发方法 .软件学报,2008,19(8)：1947-1964.http://www.jos.org.cn/1000-9825/19/1947.htm[doi:10.3724/SP.J.1001.2008.01947]
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |