基于气象因素和改进支持向量机的空气质量指数预测
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摘要
空气污染已成为人类社会面临的严峻挑战,为了构建准确的空气质量预测模型,文章首先运用统计分析方法进行相关性分析,探讨气象要素变化对空气质量的影响;针对传统支持向量机预测精确度受输入变量影响较大的弊端,采用基于熵权理论对变精度粗糙集约简进行了改进,以处理支持向量机的输入变量,提高支持向量机的预测精度;最后以沈阳市的历史气象数据和空气质量指数进行验证。与传统方法相比,所提方法能够将预测准确率由71.28%提高到77.83%,空报率和漏报率也有降低,与实际基本吻合。
Air pollution has become a serious challenge to human society,in order to construct an accurate air quality index prediction model,this paper first analyzed the impact of meteorological factors on air quality index by using Spearman rank correlation coefficient. In view of the traditional support vector machine( SVM) forecasting precision were greatly influenced by the input variable,variable precision rough set( VPRS) was modified based on entropy weight theory and the prediction accuracy of support vector machine improved. Finally,the historical meteorological data and air quality index of Shenyang were taken as the basis for verification. The results showed that the proposed method could improve the prediction accuracy from71. 28% to 77. 83% compared with the traditional method,and the rate of false report and false negative rate also decreased obviously,which verified the effectiveness of the proposed method.
Air pollution has become a serious challenge to human society,in order to construct an accurate air quality index prediction model,this paper first analyzed the impact of meteorological factors on air quality index by using Spearman rank correlation coefficient. In view of the traditional support vector machine( SVM) forecasting precision were greatly influenced by the input variable,variable precision rough set( VPRS) was modified based on entropy weight theory and the prediction accuracy of support vector machine improved. Finally,the historical meteorological data and air quality index of Shenyang were taken as the basis for verification. The results showed that the proposed method could improve the prediction accuracy from71. 28% to 77. 83% compared with the traditional method,and the rate of false report and false negative rate also decreased obviously,which verified the effectiveness of the proposed method.
引文
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[2]王淑云,节江涛,熊险平,等.城市空气质量与气象条件的关系及空气质量预报系统[J].气象科技,2006,34(6):688-692.
[3]徐晓峰,李青春,张小玲.北京一次局地重污染过程气象条件分析[J].气象科技,2005,33(6):543-547.
[4]韩婧,代志光,李文韬.西安市灰霾天气下PM2.5浓度与气象条件分析[J].环境污染与防治,2014,36(2):52-56.
[5]何建军,吴琳,毛洪钧,等.气象条件对河北廊坊城市空气质量的影响[J].环境科学研究,2016,29(6):791-799.
[6]赵宏,刘爱霞,王恺,等.基于GA-ANN改进的空气质量预测模型[J].环境科学研究,2009,22(11):1276-1281.
[7]刘杰,杨鹏,吕文生,等.基于气象因素的PM2.5质量浓度预测模型[J].山东大学学报(工学版),2015,45(6):76-83.
[8]陈淳祺,陈瑜琪,汤圣君,等.2001—2010年武汉市气象环境对空气质量影响分析[J].环境科学与技术,2013,36(5):130-133.
[9]于大江,吴艳玲,李竑积,等.东北主要城市的空气质量长期变化特征[J].环境化学,2014,33(6):956-961.
[10]刘宁微,马雁军,刘晓梅,等.沈阳地区霾与雾的观测研究[J].环境科学学报,2011,31(5):1064-1069.
[11]邹旭东,杨洪斌,张云海,等.1951─2012年沈阳市气象条件变化及其与空气污染的关系分析[J].生态环境学报,2015,24(1):76-83.
[12]曾静,王美娥,张红星.北京市夏秋季大气PM2.5浓度与气象要素的相关性[J].应用生态学报,2014,25(9):2695-2699.
[13]周兆媛,张时煌,高庆先,等.京津冀地区气象要素对空气质量的影响及未来变化趋势分析[J].资源科学,2014,36(1):191-199.
[14]Tan G Q,Yuan H Y,Liu Y,et al.Removal of lead from aqueous solution with native and chemically modified corncobs[J].J Hazard Mater,2010,174:740-745.
[15]Homagai P L,Ghimire K N,Inoue K.Adsorption behavior of heavy metals onto chemically modified sugarcane bagasse[J].Bioresource Technology,2010,101:2067-2069.
[16]Wojciech Z.Variable precision rough set model[J].Journal of Computer and System Sciences,1993,46(1):39-59.
[17]李晓丽,周小健,沈钢纲,等.不确定支持向量机在洪水预测模型中的应用[J].兰州理工大学学报,2012,38(3):107-110.
[18]陈莉,刘晓霞.离散数学[M].北京:高等教育出版社,2010:172-180.