融合协同进化离散型人工鱼群算法和多重分形的雾霾预测方法

朱旭辉, 倪志伟, 程美英, 李敬明, 金飞飞, 倪丽萍

系统工程理论与实践 ›› 2017, Vol. 37 ›› Issue (4) : 999-1010.

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系统工程理论与实践 ›› 2017, Vol. 37 ›› Issue (4) : 999-1010. DOI: 10.12011/1000-6788(2017)04-0999-12
论文

融合协同进化离散型人工鱼群算法和多重分形的雾霾预测方法

    朱旭辉1,2,3, 倪志伟1,2, 程美英1,2, 李敬明1,2, 金飞飞1,2, 倪丽萍1,2
作者信息 +

Haze prediction method based on multi-fractal dimension and co-evolution discrete artificial fish swarm algorithm

    ZHU Xuhui1,2,3, NI Zhiwei1,2, CHENG Meiying1,2, LI Jingming1,2, JIN Feifei1,2, NI Liping1,2
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文章历史 +

摘要

鉴于目前日益严重的雾霾污染,导致空气质量水平大幅下降,通过采用协同进化离散型人工鱼群算法,多重分形维数,并结合极限学习机,提出了融合协同进化离散型人工鱼群算法和多重分形的雾霾预测方法. 首先使用佳点集理论初始化种群,通过引入人工鱼游速,改进人工鱼群算法聚群,追尾和觅食行为,及对其进行离散化,并引入竞争和合作机制;其次将协同进化离散型人工鱼群算法结合多重分形维数,对雾霾数据集进行约简;最后运用极限学习机建立雾霾预测模型. 通过对北京,上海和广州三地区近两年的雾霾数据集进行实验及参数分析,实验结果表明,较其他方法,预测性能更优,具有良好的稳定性和可信性.

Abstract

Haze prediction method based on multi-fractal dimension (MFD) and co-evolution discrete artificial fish swarm algorithm (CDAFSA) is proposed by using CDAFSA, MFD and extreme learning machine (ELM), because air quality declines significantly for increasingly serious haze pollution at present. First, population of algorithm is initialized through good point set theory, swarming, chasing and foraging behavior are improved by introducing swimming speed of artificial fish, the algorithm is processed by discretization, and both competitive and collaborative operation are introduced; Second, haze datasets are reduced through taking CDAFSA and MFD; Finally, haze prediction model is established by taking ELM. By analyzing experiments on haze datasets of Beijing, Shanghai and Guangzhou for nearly two years and parameters in detail, experimental results indicate that the proposed prediction method is superior to traditional methods, has relatively high stability and credibility.

关键词

人工鱼群算法 / 协同进化 / 多重分形维数 / 极限学习机 / 雾霾预测

Key words

artificial fish swarm algorithm / co-evolution / multi-fractal dimension / ELM / haze prediction

引用本文

导出引用
朱旭辉 , 倪志伟 , 程美英 , 李敬明 , 金飞飞 , 倪丽萍. 融合协同进化离散型人工鱼群算法和多重分形的雾霾预测方法. 系统工程理论与实践, 2017, 37(4): 999-1010 https://doi.org/10.12011/1000-6788(2017)04-0999-12
ZHU Xuhui , NI Zhiwei , CHENG Meiying , LI Jingming , JIN Feifei , NI Liping. Haze prediction method based on multi-fractal dimension and co-evolution discrete artificial fish swarm algorithm. Systems Engineering - Theory & Practice, 2017, 37(4): 999-1010 https://doi.org/10.12011/1000-6788(2017)04-0999-12
中图分类号: TP391   

参考文献

[1] Deng H, Tan H B, Li F, et al. Impact of relative humidity on visibility degradation during a haze event: A case study[J]. Science of the Total Environment, 2016, 569: 1149-1158.
[2] Shen X J, Sun J Y, Zhang X Y, et al. Characterization of submicron aerosols and effect on visibility during a severe haze-fog episode in Yangtze River Delta, China[J]. Atmospheric Environment, 2015, 120: 307-316.
[3] Lin Y F, Huang K, Zhuang G S, et al. A multi-year evolution of aerosol chemistry impacting visibility and haze formation over an Eastern Asia megacity, Shanghai[J]. Atmospheric Environment, 2014, 92: 76-86.
[4] Luo Z X, Gao M R, Luo X S, et al. National pattern for heavy metal contamination of topsoil in remote farmland impacted by haze pollution in China[J]. Atmospheric Research, 2016, 170: 34-40.
[5] McLaren J, Williams I D. The impact of communicating information about air pollution events on public health[J]. Science of the Total Environment, 2015, 538: 478-491.
[6] 谢元博, 陈娟, 李巍,等. 雾霾重污染期间北京居民对高浓度PM2.5持续暴露的健康风险及其损害价值评估[J]. 环境科学, 2014, 35(1): 1-8. Xie Y B, Chen J, Li W, et al. An assessment of PM2.5 related health risks and impaired values of Beijing residents in a consecutive high-level exposure during heavy haze days[J]. Environmental Science, 2014, 35(1): 1-8.
[7] Quan J N, Liu Q, Li X, et al. Effect of heterogeneous aqueous reactions on the secondary formation of inorganic aerosols during haze events[J]. Atmospheric Environment, 2015, 122: 306-312.
[8] Xu W B, Jing S C, Yu W J, et al. A comparison between Bayes discriminant analysis and logistic regression for prediction of debris flow in Southwest Sichuan, China[J]. Geomorphology, 2013, 201(1): 45-51.
[9] Umar Z, Pradhan B, Ahmad A, et al. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia[J]. Catena, 2014, 118: 124-135.
[10] Marvin H J P, Kleter G A, Noordam M Y, et al. Proactive systems for early warning of potential impacts of natural disasters on food safety: Climate-change-induced extreme events as case in point[J]. Food Control, 2013, 34(2): 444-456.
[11] 刘双印, 徐龙琴, 李道亮. 基于粗糙集融合支持向量机的水质预警模型[J]. 系统工程理论与实践, 2015, 35(6): 1617-1624. Liu S Y, Xu L Q, Li D L. Water quality early-warning model based on support vector machine optimized by rough set algorithm[J]. Systems Engineering——Theory & Practice, 2015, 35(6): 1617-1624.
[12] Koyuncugil A S, Ozgulbas N. Financial early warning system model and data mining application for risk detection[J]. Expert Systems with Applications, 2012, 39(6): 6238-6253.
[13] 倪志伟, 薛永坚, 倪丽萍,等. 基于流行学习的多核SVM财务预警方法研究[J]. 系统工程理论与实践, 2014, 34(10): 2666-2674. Ni Z W, Xue Y J, Ni L P, et al. Research of multiple kernel SVM based on manifold learning in financial distress prediction[J]. Systems Engineering——Theory & Practice, 2014, 34(10): 2666-2674.
[14] 杨迎心, 冯志勇, 饶国政,等. 基于模糊综合评价构建物流运输预警模型[J]. 计算机应用, 2011, 31(10): 2844-2848. Yang Y X, Feng Z Y, Rao G Z, et al. Early-warning model of logistics transport based on fuzzy comprehensive evaluation[J]. Journal of Computer Applications, 2011, 31(10): 2844-2848.
[15] 王伟, 沈振中, 李桃凡. 遗传算法与自适应粒子群算法耦合的大坝安全预警评价模型[J]. 岩土工程学报, 2009, 31(8): 1242-1247. Wang W, Shen Z Z, Li T F. Safety early warning evaluation model for dams based on coupled method of genetic algorithm and adapting particle swarm optimization algorithm[J]. Chinese Journal of Geotechnical Engineering, 2009, 31(8): 1242-1247.
[16] Pérez-Suárez R, López-Menéndez A J. Growing green? Forecasting CO2 emissions with environmental Kuznets curves and logistic growth models[J]. Environmental Science & Policy, 2015, 54: 428-437.
[17] Reikard G. Forecasting volcanic air pollution in Hawaii: Tests of time series models[J]. Atmospheric Environment, 2012, 60: 593-600.
[18] Mishra D, Goyal P, Upadhyay A. Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of Delhi, India[J]. Atmospheric Environment, 2015, 102: 239-248.
[19] Bai Y, Li Y, Wang X X, et al. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions[J]. Atmospheric Pollution Research, 2016, 7(3): 557-566.
[20] Feng X, Li Q, Zhu Y J, et al. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation[J]. Atmospheric Environment, 2015, 107: 118-128.
[21] García Nieto P J, Combarro E F, del Coz Díaz J J, et al. A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study[J]. Applied Mathematics and Computation, 2013, 219(17): 8923-8937.
[22] Dumitrache R C, Iriza A, Maco B A, et al. Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory[J]. Atmospheric Environment, 2016, 143: 278-289.
[23] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70: 489-501.
[24] Huang G B, Zhu Q Y, Siew C K. Universal appximation using incremental constructive feed-forward network with random hidden nodes[J]. IEEE Transactions on Neural Network, 2006, 17: 879-892.
[25] Qiu S S, Gao L P, Wang J. Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice[J]. Journal of Food Engineering, 2015, 144: 77-85.
[26] Wan C, Mita A. Early warning of hazard for pipelines by acoustic recognition using principal component analysis and one-class support vector machines[J]. Smart Structures and Systems, 2010, 6(4): 405-421.
[27] Balka R, Buczolich Z, Elekes M. A new fractal dimension: The topological Hausdorff dimension[J]. Advances in Mathematics, 2015, 274: 881-927.
[28] Zhang C, Ni Z W, Ni L P, et al. Feature selection method based on multi-fractal dimension and harmony search algorithm and its application[J]. International Journal of Systems Science, 2016, 47(14): 3476-3486.
[29] 朱旭辉, 倪志伟, 程美英. 变步长自适应的改进人工鱼群算法[J]. 计算机科学, 2015, 42(2): 210-216. Zhu X H, Ni Z W, Cheng M Y. Self-adaptive improved artificial fish swarm algorithm with changing step[J]. Computer Science, 2015, 42(2): 210-216.
[30] 张铃, 张钹. 佳点集遗传算法[J]. 计算机学报, 2001, 24(9): 917-922. Zhang L, Zhang B. Good point set based genetic algorithm[J]. Chinese Journal of Computers, 2001, 24(9): 917-922.
[31] Yan H W, Cao Y L, Yang J X. Statistical tolerance analysis based on good point set and homogeneous transform matrix[J]. Procedia CIRP, 2016, 43: 178-183.
[32] 段其昌, 唐若笠, 徐宏英,等. 粒子群优化鱼群算法仿真分析[J]. 控制与决策, 2013, 28(9): 1436-1440. Duan Q C, Tang R L, Xu H Y, et al. Simulation analysis of the fish swarm algorithm optimized by PSO[J]. Control and Decision, 2013, 28(9): 1436-1440.
[33] 黄翰, 郝志峰, 吴春国,等. 蚁群算法的收敛速度分析[J]. 计算机学报, 2007, 30(8): 1344-1353. Huang H, Hao Z F, Wu C G, et al. The convergence speed of ant colony optimization[J]. Chinese Journal of Computers, 2007, 30(8): 1344-1353.
[34] 倪志伟, 肖宏旺, 伍章俊,等. 基于改进离散型萤火虫群优化算法和分形维数的属性选择方法[J]. 模式识别与人工智能, 2013, 26(12): 1169-1178. Ni Z W, Xiao H W, Wu Z J, et al. Attribute selection method based on improved discrete glowworm swarm optimization and fractal dimension[J]. Pattern Recognition and Artificial Intelligence, 2013, 26(12): 1169-1178.
[35] Wang S, Liao T T, Wang L L, et al. Process analysis of characteristics of the boundary layer during a heavy haze pollution episode in an inland megacity, China[J]. Journal of Environmental Sciences, 2016, 40: 138-144.
[36] Zhang H, Xie B, Zhao S Y, et al. PM2.5 and tropospheric O3 in China and an analysis of the impact of pollutant emission control[J]. Advances in Climate Change Research, 2014, 5(3): 136-141.
[37] Qiao T, Zhao M F, Xiu G L, et al. Simultaneous monitoring and compositions analysis of PM10 and PM2.5 in Shanghai: Implications for characterization of haze pollution and source apportionment[J]. Science of the Total Environment, 2016, 557: 386-394.
[38] Azad M A K, Rocha A M A C, Fernandes E M G P. Improved binary artificial fish swarm algorithm for the 0-1 multidimensional knapsack problems[J]. Swarm and Evolutionary Computation, 2014, 14: 66-75.
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