自适应神经模糊推理系统在交通污染物浓度预测中的应用
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摘要
城市交通带来的废气排放已经成为城市大气污染的主要来源之一。交通污染问题的成因和机理较为复杂,变化规律具有较强非线性和周期性特征。将自适应神经模糊推理系统(adaptive neuro fuzzy inference system,ANFIS)应用于交通污染物浓度时序数据预测时呈现出良好的泛化能力。本文以长沙市CO小时浓度数据为研究目标,通过分析CO浓度时序数据的自相关性、偏自相关性,以及交通流对CO浓度的时滞性影响,确定ANFIS预测模型的输入变量。结果表明,相较于传统的时间序列预测模型以及机器学习模型,ANFIS模型预测结果具有更高的精度,能够对交通环境污染进行预测及预警,为防止城市灾害性大气污染事件发生奠定理论研究基础并提供有效决策支持。
The exhaust emissions from urban traffic have become one of the main sources of urban air pollution.The cause and mechanism of traffic-related pollution are complicated, and the change law of traffic-related pollution has strong nonlinear and periodic characteristics. When adaptive neural fuzzy inference system(ANFIS) is applied for predicting time series data of traffic-related pollution,the good generalization ability is demonstrated. This paper is to study the CO hourly concentration data in Changsha.After the autocorrelation and partial autocorrelation of CO hourly concentration data and the time delay effect of traffic flow on CO hourly concentration have been analyzed,the input variables of the ANFIS are determined.The results indicate that the ANFIS has higher accuracy than traditional time series prediction model and machine learning model.ANFIS can provide prediction and early warning of traffic-related pollution,establishing the foundation of theoretical research and providing effective decision support for prevention of urban catastrophic air pollution events.
The exhaust emissions from urban traffic have become one of the main sources of urban air pollution.The cause and mechanism of traffic-related pollution are complicated, and the change law of traffic-related pollution has strong nonlinear and periodic characteristics. When adaptive neural fuzzy inference system(ANFIS) is applied for predicting time series data of traffic-related pollution,the good generalization ability is demonstrated. This paper is to study the CO hourly concentration data in Changsha.After the autocorrelation and partial autocorrelation of CO hourly concentration data and the time delay effect of traffic flow on CO hourly concentration have been analyzed,the input variables of the ANFIS are determined.The results indicate that the ANFIS has higher accuracy than traditional time series prediction model and machine learning model.ANFIS can provide prediction and early warning of traffic-related pollution,establishing the foundation of theoretical research and providing effective decision support for prevention of urban catastrophic air pollution events.
引文
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[4] Alvarez-Vazquez L J,Garcia-Chan N.Numerical simulation of air pollution due to traffic flow in urban networks[J].Journal of Computational and Applied Mathematics,2017,326:44～61.
[5] Zhang Y,et al.Real-time air quality forecasting,Part I:history,techniques,and current status[J].Atmospheric Environment,2012,60:632～655.
[6] Noori R,Hoshyaripour G.Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration[J].Atmospheric Environment,2010,44:476～482.
[7] 王平,张红,秦作栋.基于wave-SVM的PM10浓度时序数据预测[J].环境科学,2017,38(8):3153～3161.
[8] Jang.ANFIS:Adaptive-network-based fuzzy inference system[J].IEEE Transactions on Systems,Man,and Cybernetics,1993,23:665～685.
[9] Md.Rafiul Hassan,Kotagiri Ramamohanarao,Joarder Kamruzzaman.A HMM-based adaptive fuzzy inference system for stock market forecasting[J].Neurocomputing,2013,104:10～25.
[10] Azadeh A,Asadzadeh S M,Mirseraji G H,Saberi M.An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data[J].Technological Forecasting & Social Change,2015,91:47～63.
[11] Sefeedpari P,Rafiee S,Akram A.Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach[J].Computers and Electronics in Agriculture,2016,131:10～19.
[12] 贺克斌,霍红.道路机动车排放模型技术方法与应用[M].北京:科学出版社,2014.
[13] Wu Y,Wang R,Zhou Y.On-road vehicle emission control in Beijing:Past,present,and future[J].Environmental Science & Technology,2011,45(1):147～153.
[14] 邹超,吴琳,李笑语.南京市交通流与NO2、CO时空分布特征关系研究[J].环境科学学报,2017,37(10):3895～3905.
[15] Box G E P,Jenkins G M,Reinsel G C.Time series analysis:Forecasting and control[M].Wiley Press,2008.
[16] Chiu S L.Fuzzy model identification based on cluster estimation[J].Intelligent Fuzzy System,2008,2(3):267～278.
[17] 刘铭,张双全,何禹德.基于改进型模糊神经网络的信用卡客户违约预测[J].模糊系统与数学,2017,31(1):144～148.
[18] 黄华,张金喜,宋艳萍.基于模糊时间序列模型的新疆季度降水预测研究[J].模糊系统与数学,2016,30(4):177～182.