临路住宅小区的噪声空间分布模型研究
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摘要
随着城市的发展和道路基础设施的逐步完善,以及人们对环保意识的增强和对环境质量要求的提高,环境噪声问题已成为越来越受关注的环境问题。因此在项目建设之前进行环境噪声的预测和评估是十分必要的,它对环境污染控制、管理、规划等方面均具有重要意义。
住宅小区是人们日常活动的最主要场所,但是却受到越来越多噪声的干扰。对于规划中的项目,我们希望知道其建成后的噪声分布情况,需要进行噪声预测。本文主要学习了通用的噪声预测模型FHWA模式和神经网络的基本原理,选择了BP神经网络作为建立噪声预测模型的基本理论和方法,尝试性的将BP神经网络用于创建噪声预测模型,并且进行了神经网络模型的优化。以某一小区的设计方案为例,运用该噪声预测模型对其进行噪声预测,比较两种模式下的预测精度,经过分析预测结果可知预测结果能较好的模拟该设计方案落成后的噪声分布情况。将噪声预测模型与GIS专业软件相结合,较为直观和逼真的显示了预测结果,根据预测结果调整设计方案,从而到达辅助规划决策的目的。论文的创新点在于:(1)用BP神经网络建立了一个面向小区域的噪声预测模型,补充并发展了噪声预测的新方法;(2)该噪声预测模型与3DCM相结合,在建筑群中对声场进行非线性建模。实验证明基于神经网络的噪声预测模型具有自组织、全局优化与容错学习等能力,通过样本学习可使预测模型有良好的预测能力;噪声预测模型与3DCM的结合,模拟了噪声在建筑群中的分布,更好地将噪声信息进行了表达和分析应用,为政府部门进行城市规划、管理及决策提供科学有效的证据支持。
With the urban development and the improvement of road infrastructure gradually, as well as people's awareness of environmental protection and improvement of environmental quality, environmental noise has become more and more affected by environmental concerns. Therefore, it is necessary to predict and evaluate the environmental noise before the project construction, and it’s of great significance for environmental pollution control, management and planning. etc.
Residential area is important for people’s daily activities, but it is suffered by more and more noise. After completion of the planning project, we want to know the noise distribution and predict noise. According to the predictions, analyze spatial distribution, time distribution and the probability distribution characteristics of the noise objectively and effectively with help of computer simulation technology, and adjust the design based on the Urban Regional Environmental Noise Standards, so as to control the noise at Planning Stage effectively, Further more, the purpose of optimizing residential environment can be achieved. There are many problems of noise prediction models though each has its own advantages on specific applications. This paper uses the advantages of the neural network toestablish a prediction model based on the BP neural network. Innovation of this paper are: (1) Base on BP neural network to establishment a noise prediction model for small area, complement and developed a new method of noise prediction; (2) the noise prediction model combined with 3DCM , complex the sound field on nonlinear modeling. Experimental results shows that based on neural network noise prediction model has self-organization, global optimization and fault-tolerant learning abilities, neural networks can learn through the sample noise in the buildings of the acoustic field in non-linear modeling, which has a good predictive ability; The noise prediction model combined with 3DCM is a good simulation of noise in buildings in the distribution. Prediction of noise based on neural network combine with three-dimensional city models (3DCM), is better for information expression and analysis applications, offer scientific and effective supporting evidence to government departments for urban planning, management and decision-making.
住宅小区是人们日常活动的最主要场所,但是却受到越来越多噪声的干扰。对于规划中的项目,我们希望知道其建成后的噪声分布情况,需要进行噪声预测。本文主要学习了通用的噪声预测模型FHWA模式和神经网络的基本原理,选择了BP神经网络作为建立噪声预测模型的基本理论和方法,尝试性的将BP神经网络用于创建噪声预测模型,并且进行了神经网络模型的优化。以某一小区的设计方案为例,运用该噪声预测模型对其进行噪声预测,比较两种模式下的预测精度,经过分析预测结果可知预测结果能较好的模拟该设计方案落成后的噪声分布情况。将噪声预测模型与GIS专业软件相结合,较为直观和逼真的显示了预测结果,根据预测结果调整设计方案,从而到达辅助规划决策的目的。论文的创新点在于:(1)用BP神经网络建立了一个面向小区域的噪声预测模型,补充并发展了噪声预测的新方法;(2)该噪声预测模型与3DCM相结合,在建筑群中对声场进行非线性建模。实验证明基于神经网络的噪声预测模型具有自组织、全局优化与容错学习等能力,通过样本学习可使预测模型有良好的预测能力;噪声预测模型与3DCM的结合,模拟了噪声在建筑群中的分布,更好地将噪声信息进行了表达和分析应用,为政府部门进行城市规划、管理及决策提供科学有效的证据支持。
With the urban development and the improvement of road infrastructure gradually, as well as people's awareness of environmental protection and improvement of environmental quality, environmental noise has become more and more affected by environmental concerns. Therefore, it is necessary to predict and evaluate the environmental noise before the project construction, and it’s of great significance for environmental pollution control, management and planning. etc.
Residential area is important for people’s daily activities, but it is suffered by more and more noise. After completion of the planning project, we want to know the noise distribution and predict noise. According to the predictions, analyze spatial distribution, time distribution and the probability distribution characteristics of the noise objectively and effectively with help of computer simulation technology, and adjust the design based on the Urban Regional Environmental Noise Standards, so as to control the noise at Planning Stage effectively, Further more, the purpose of optimizing residential environment can be achieved. There are many problems of noise prediction models though each has its own advantages on specific applications. This paper uses the advantages of the neural network toestablish a prediction model based on the BP neural network. Innovation of this paper are: (1) Base on BP neural network to establishment a noise prediction model for small area, complement and developed a new method of noise prediction; (2) the noise prediction model combined with 3DCM , complex the sound field on nonlinear modeling. Experimental results shows that based on neural network noise prediction model has self-organization, global optimization and fault-tolerant learning abilities, neural networks can learn through the sample noise in the buildings of the acoustic field in non-linear modeling, which has a good predictive ability; The noise prediction model combined with 3DCM is a good simulation of noise in buildings in the distribution. Prediction of noise based on neural network combine with three-dimensional city models (3DCM), is better for information expression and analysis applications, offer scientific and effective supporting evidence to government departments for urban planning, management and decision-making.
引文
[1]李红星,赵军.基于GIS的区域环境噪声评价方法研究[J].遥感技术与用,2006,21(4):363-367.
[2]李木纲,陶澎,林健枝.城市居住小区的公路交通噪声预测与规划[J].城市环境与城市生态. 1999-4,12(2): 57~60.
[3]李本纲,陶澎.城市居住小区交通噪声总体评价与防治对策研究[J].环境科学学报. 2002-5, 22(3): 397~401.
[4]朱荣涛.交通噪声预测模型在城市主干路上的应用研究[D].吉林:吉林大学,2007.
[5]张为民.城市环境噪声影响评价探讨[J].电力环境保护.2008-6,24(3):48~49.
[6]2008年中国环境状况公报[J].国家环境保护总局, 2009, (8) .
[7]张继萍,吴硕贤.人工神经网络在道路交通噪声预测中的应用[J].环境科学学报.1998-9,18(5):471~477.
[8]李本纲,陶澍.城市道路交通噪声评价方法研究进展[J].交通环保,2001,22(5):5~9.
[9]姜海涛,夏青海等.道路交通噪声预测模型及其应用[J].环境科学,1989,10(6):62~66.
[10]吴硕贤,Kittinger E.考虑声散射的街道交通噪声预报模型[J].环境科学学报,1996,16(3):364~371.
[11]陈子明,路德明等.高速高架复合道路交通噪声的分析和预测[J].环境科学,1994,15(2):24~28.
[12]王国平.灰色系统理论在城市交通噪声预测和绝对关联度分析中的应用[J].中国环境科学,1996,16(1): 56~59.
[13]Barry T M, Reagan J A. FHWA Highway Traffic Noise Prediction Model[M]. Washington DC: FHWA-RD-77-108,1973
[14]苏淡,赵越品,吴硕贤.临街住宅小区环境噪声预测[J].噪声与振动控制.2007,第s1期:55-58.
[15]石垚,巨天珍,温飞等.基于FHWA的兰州市道路交通噪声预测模型的建立[J].环境科学学报.2006.26(9): 1568-1575.
[16]黄有兴,张部俊.区域环境噪声二维分布灰色系统模型预测[J].环境析染与防治.1993,15(3):42~45.
[17][日]市原薰,枝村俊郎编.工惠普,林耀中译.逆路设施工程学[M].北京:人民交通出版社,1982: 271
[18] Shuoxian Wu.A Simple Method for Predicting Kerbside L10 Level from a Free Multi_type vehicular flow[J].Appl Acoustics 1987,20:15.
[19]曲格平等编.城市环境污染与防治[M].北京:中国环境科学出版社,1989: 60.
[20] Watkins L H.Environmental Impact of Roads and Traffic[M]. England: Applied Science Puhlishers,1981.
[21]朱军辉.三维城市建模与基本作战性能分析[D].郑州:解放军信息工程人学.2006
[22]谭仁春,彭清山.基于3DCM的专题数据可视化研究[J].地理空间信息. 2007-12,5(6):74~76.
[23]王勇,李朝奎.三维城市模型(3DCM)的研究与应用现状[J].湘潭师范学院学报(自然科学版). 2007-12, 29(4):127~131.
[24]李朝奎,王勇,鲍振洪.基于3DCM的城市覆盖面模拟及建筑物空间插值方法[J].湖南科技大学学报(自然科学版). 2008-9,23(3):87~89.
[25]谭仁春,李长辉,王红新.三维城市模型的构建及其在噪音污染分析中的应用[J].测绘科学.2006.31(3): 101-103.
[26]李朝奎,朱庆,黄铎.三维城市模型辅助空间决策支持的研究思路[J].湘潭矿业学院学报.2003.18(3): 73~76.
[27]张邦俊,翟国庆.环境噪声学[M].江苏:浙江大学出版社,2001.
[28]廖俊彦.环境噪声预测模式的探讨与软件设计[D].安徽:合肥工业大学,2008.
[29]许肖梅.声学基础[M].北京:北京科学出版社,2003.
[30]PAMANIKABUD P,PAOPRAYOON S.Predicting mass rapid transit noise levels on Management. an elevated station[J].353-362Journal of Environmental2003,67.
[3]姚展予.声波在湍流大气中的散射衰减.应用气象学报[J].1994, 5(1):82-86.
[32]潘乃先,刘建强.声波逾量衰减的估算.大气科学[J].1991,15(1):116-122.
[33]国家《城市区域环境噪声标准》(GB3096- 93).1993.
[34]涂瑞和,刘光辉,柳至和.关于应用美国FHWA公路噪声预测模式的几个问题[J].环境工程.10(2):41`43.
[35]王弘.高速公路交通噪声预测模式在典型路段的应用效果及修正[D].广州:暨南大学,2006.
[36]林巨,陈子明,王欢.公路交通噪声预测的神经网络模型[J].城市环境与城市生态.2001,14(1):4~6.
[37]南晋华.决策神经网络模型及应用研究[D].武汉:华中科技大学,2008.
[38]闻新,周露,李翔等.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003.
[39]陈可可,姚建,徐磊.BP人工神经网络模型在拉萨道路交通噪声预测中的应用[J].环境科学与管理,2006, 31( 5):78-81.
[40]魏海坤.神经网络结果的设计的理论与方法[M].北京:国防工业出版社,2005.
[4]李本纲,陶澎.道路交通噪声预测模型研究进展[J].环境科学研究.2002-2,15(2):56~59.
[42]GB3096-93.城市区域环境噪声标准.
[43]黄涛,李宁,阿苏阿芝兰.州市城关区“民族花苑”住宅小区环境噪声的监测与评价[J].西北民族大学学报(自然科学版).2003-3, 28(65):30~35.
[44]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005.3:0~322.
[45]何毅.基于BP神经网络电容法刨花含水率测试仪的研究与开发[D].南京:南京林业大学,2006.
[2]李木纲,陶澎,林健枝.城市居住小区的公路交通噪声预测与规划[J].城市环境与城市生态. 1999-4,12(2): 57~60.
[3]李本纲,陶澎.城市居住小区交通噪声总体评价与防治对策研究[J].环境科学学报. 2002-5, 22(3): 397~401.
[4]朱荣涛.交通噪声预测模型在城市主干路上的应用研究[D].吉林:吉林大学,2007.
[5]张为民.城市环境噪声影响评价探讨[J].电力环境保护.2008-6,24(3):48~49.
[6]2008年中国环境状况公报[J].国家环境保护总局, 2009, (8) .
[7]张继萍,吴硕贤.人工神经网络在道路交通噪声预测中的应用[J].环境科学学报.1998-9,18(5):471~477.
[8]李本纲,陶澍.城市道路交通噪声评价方法研究进展[J].交通环保,2001,22(5):5~9.
[9]姜海涛,夏青海等.道路交通噪声预测模型及其应用[J].环境科学,1989,10(6):62~66.
[10]吴硕贤,Kittinger E.考虑声散射的街道交通噪声预报模型[J].环境科学学报,1996,16(3):364~371.
[11]陈子明,路德明等.高速高架复合道路交通噪声的分析和预测[J].环境科学,1994,15(2):24~28.
[12]王国平.灰色系统理论在城市交通噪声预测和绝对关联度分析中的应用[J].中国环境科学,1996,16(1): 56~59.
[13]Barry T M, Reagan J A. FHWA Highway Traffic Noise Prediction Model[M]. Washington DC: FHWA-RD-77-108,1973
[14]苏淡,赵越品,吴硕贤.临街住宅小区环境噪声预测[J].噪声与振动控制.2007,第s1期:55-58.
[15]石垚,巨天珍,温飞等.基于FHWA的兰州市道路交通噪声预测模型的建立[J].环境科学学报.2006.26(9): 1568-1575.
[16]黄有兴,张部俊.区域环境噪声二维分布灰色系统模型预测[J].环境析染与防治.1993,15(3):42~45.
[17][日]市原薰,枝村俊郎编.工惠普,林耀中译.逆路设施工程学[M].北京:人民交通出版社,1982: 271
[18] Shuoxian Wu.A Simple Method for Predicting Kerbside L10 Level from a Free Multi_type vehicular flow[J].Appl Acoustics 1987,20:15.
[19]曲格平等编.城市环境污染与防治[M].北京:中国环境科学出版社,1989: 60.
[20] Watkins L H.Environmental Impact of Roads and Traffic[M]. England: Applied Science Puhlishers,1981.
[21]朱军辉.三维城市建模与基本作战性能分析[D].郑州:解放军信息工程人学.2006
[22]谭仁春,彭清山.基于3DCM的专题数据可视化研究[J].地理空间信息. 2007-12,5(6):74~76.
[23]王勇,李朝奎.三维城市模型(3DCM)的研究与应用现状[J].湘潭师范学院学报(自然科学版). 2007-12, 29(4):127~131.
[24]李朝奎,王勇,鲍振洪.基于3DCM的城市覆盖面模拟及建筑物空间插值方法[J].湖南科技大学学报(自然科学版). 2008-9,23(3):87~89.
[25]谭仁春,李长辉,王红新.三维城市模型的构建及其在噪音污染分析中的应用[J].测绘科学.2006.31(3): 101-103.
[26]李朝奎,朱庆,黄铎.三维城市模型辅助空间决策支持的研究思路[J].湘潭矿业学院学报.2003.18(3): 73~76.
[27]张邦俊,翟国庆.环境噪声学[M].江苏:浙江大学出版社,2001.
[28]廖俊彦.环境噪声预测模式的探讨与软件设计[D].安徽:合肥工业大学,2008.
[29]许肖梅.声学基础[M].北京:北京科学出版社,2003.
[30]PAMANIKABUD P,PAOPRAYOON S.Predicting mass rapid transit noise levels on Management. an elevated station[J].353-362Journal of Environmental2003,67.
[3]姚展予.声波在湍流大气中的散射衰减.应用气象学报[J].1994, 5(1):82-86.
[32]潘乃先,刘建强.声波逾量衰减的估算.大气科学[J].1991,15(1):116-122.
[33]国家《城市区域环境噪声标准》(GB3096- 93).1993.
[34]涂瑞和,刘光辉,柳至和.关于应用美国FHWA公路噪声预测模式的几个问题[J].环境工程.10(2):41`43.
[35]王弘.高速公路交通噪声预测模式在典型路段的应用效果及修正[D].广州:暨南大学,2006.
[36]林巨,陈子明,王欢.公路交通噪声预测的神经网络模型[J].城市环境与城市生态.2001,14(1):4~6.
[37]南晋华.决策神经网络模型及应用研究[D].武汉:华中科技大学,2008.
[38]闻新,周露,李翔等.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003.
[39]陈可可,姚建,徐磊.BP人工神经网络模型在拉萨道路交通噪声预测中的应用[J].环境科学与管理,2006, 31( 5):78-81.
[40]魏海坤.神经网络结果的设计的理论与方法[M].北京:国防工业出版社,2005.
[4]李本纲,陶澎.道路交通噪声预测模型研究进展[J].环境科学研究.2002-2,15(2):56~59.
[42]GB3096-93.城市区域环境噪声标准.
[43]黄涛,李宁,阿苏阿芝兰.州市城关区“民族花苑”住宅小区环境噪声的监测与评价[J].西北民族大学学报(自然科学版).2003-3, 28(65):30~35.
[44]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005.3:0~322.
[45]何毅.基于BP神经网络电容法刨花含水率测试仪的研究与开发[D].南京:南京林业大学,2006.