基于改进萤火虫寻优支持向量机的PM2.5预测
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摘要
针对现有PM2.5浓度预测误差较大的问题,提出一种基于改进萤火虫寻优支持向量机的预测模型(IFASVM).该模型引入邻域搜索和可变步长策略改进萤火虫算法,利用改进FA对SVM的参数C、ε和γ寻优,用最优参数SVM模型预测太原市PM2.5值.其中邻域搜索策略能为参数优化提供更多更精确的候选解;可变步长可动态调整算法搜索步长,加速收敛,平衡FA的全局和局部搜索能力.将IFA-SVM预测值与萤火虫算法—支持向量机(FA-SVM)、遗传算法—支持向量机(GA-SVM)、粒子群算法—支持向量机(PSO-SVM)相比较.结果表明较其他方法, IFA-SVM模型对太原市未来一天和三天的PM2.5值都取得了更精确的预测性能.
Aiming at the problem of large deviation in existing PM2.5 concentration prediction,a novel model based on Improved Firefly Algorithm optimization SVM(IFA-SVM) was proposed.In this model,two neighborhood search strategies and variable step size mechanism were employed to improve FA.The IFA was applied to optimize the SVM parameters(C,,and),and an outstanding model was constructed to forecast PM2.5 concentrations in Taiyuan.The neighborhood search strategies can provide better candidate solutions;search step size was dynamically tuned by using variable step size strategy to accelerate convergence and obtain a trade-off between exploration and exploitation.The performance of the proposed IFA-SVM model has been compared with FA-SVM,Genetic Algorithm(GA)-SVM,and Particle Swarm Optimization(PSO)-SVM.Experimental results show that the proposed IFA-SVM model has achieved more accurate performance for PM2.5 forecasts in 1 day ahead and 3 days ahead compared to other method.
Aiming at the problem of large deviation in existing PM2.5 concentration prediction,a novel model based on Improved Firefly Algorithm optimization SVM(IFA-SVM) was proposed.In this model,two neighborhood search strategies and variable step size mechanism were employed to improve FA.The IFA was applied to optimize the SVM parameters(C,,and),and an outstanding model was constructed to forecast PM2.5 concentrations in Taiyuan.The neighborhood search strategies can provide better candidate solutions;search step size was dynamically tuned by using variable step size strategy to accelerate convergence and obtain a trade-off between exploration and exploitation.The performance of the proposed IFA-SVM model has been compared with FA-SVM,Genetic Algorithm(GA)-SVM,and Particle Swarm Optimization(PSO)-SVM.Experimental results show that the proposed IFA-SVM model has achieved more accurate performance for PM2.5 forecasts in 1 day ahead and 3 days ahead compared to other method.
引文
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5程兴宏,刁志刚,胡江凯,等.基于CMAQ模式和自适应偏最小二乘回归法的中国地区PM25浓度动力-统计预报方法研究.环境科学学报,2016, 36(8):2771-2782.
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13杨孟英.基于支持向量机的中文文本分类研究[硕士学位论文].保定:华北电力大学,2017.
14杨海,丁毅,沈海斌.基于改进萤火虫算法的SVM核参数选取.计算机应用与软件,2015,32(6):256-259.[doi:10.3969/j.issn.1000-386x.2015.06.063]
15 Kanimozhi T, Latha K. An integrated approach to region based image retrieval using firefly algorithm and support vector machine. Neurocomputing, 2015, 151:1099-1 111.[doi:10.1016/j.neucom.2014.07.078]
16 Ch S, Sohani SK, Kumar D, et al. A support vector machinefirefly algorithm based forecasting model to determine malaria transmission. Neurocomputing, 2014, 129:279-288.[doi:10.1016/j.neucom.2013.09.030]
17王晓静,彭虎,邓长寿,等.基于均匀局部搜索和可变步长的萤火虫算法.计算机应用,2018, 38(3):715-721.
18 Wang H, Cui ZH, Sun H, et al. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Computing, 2017,21(18):5325-5339.[doi:10.1007/s00500-016-2116-z]
19 Yu SH, Zhu SL, Ma Y, et al. A variable step size firefly algorithm for numerical optimization. Applied Mathematics and Computation, 2015, 263:214-220.[doi:10.1016/j.amc.2015.04.065]
20谢金鑫.基于数据挖掘的雾霾天气影响因子研究[硕士学位论文].成都:电子科技大学,2014.
21 Zhang CH, Tian YJ, Deng NY. The new interpretation of support vector machines on statistical learning theory.Science in China Series A:Mathematics, 2010, 53(1):151-164.
22 Fister I, Fister Jr I, Yang XS, et al. A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation,2013. 13:34-46.[doi:10.1016/j.swevo.2013.06.001]
2周艳军.PM2.5的污染危害及预防对策.产业与科技论坛,2013, 12(11):114-115.[doi:10.3969/j.issn. 1673-5641.2013.11.064]
3 Jian L, Zhao Y, Zhu YP, et al. An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou,China. Science of the Total Environment, 2012,426:336-345.[doi:10.1016/j.scitotenv.2012.03.025]
4郎艺超,肖璐,Christakos G.基于SARIMA模型和普通Kriging法对杭州市主城区PM2 5短期预测和制图.环境科学学报,2018, 38(1):62-70.
5程兴宏,刁志刚,胡江凯,等.基于CMAQ模式和自适应偏最小二乘回归法的中国地区PM25浓度动力-统计预报方法研究.环境科学学报,2016, 36(8):2771-2782.
6 Perez P, Trier A, Reyes J. Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago,Chile. Atmosphere Environment, 2000, 34(8):1189-1196.[doi:10.1016/S1352-2310(99)00316-7]
7 Feng X, Li Q,Zhu YJ, et al. Artificial neural networks forecasting of PM2 5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 2015, 107:118-128.[doi:10.1016/j.atmosenv.2015.02.030]
8艾洪福,石莹.基于BP人工神经网络的雾霾天气预测研究.计算机仿真,2015, 32(1):402-405, 415.[doi:10.3969/j.issn.1006-9348.2015.01.085]
9陈俏,曹根牛,陈柳.支持向量机应用于大气污染物浓度预测.计算机技术与发展,2010, 20(1):250-252.[doi:10.3969/j.issn.1673-629X.2010.01.065]
10 Wu ZH. SVR smoggy forecast model based on data mining.International Journal of Science, 2015, 2(9):120-125.
11朱霄珣,徐搏超,焦宏超,等.遗传算法对SVR风速预测模型的多参数优化.电机与控制学报,2017, 21(2):70-75.
12戴李杰,张长江,马雷鸣.基于机器学习的PM2.5短期浓度动态预报模型.计算机应用,2017, 37(11):3057-3063.
13杨孟英.基于支持向量机的中文文本分类研究[硕士学位论文].保定:华北电力大学,2017.
14杨海,丁毅,沈海斌.基于改进萤火虫算法的SVM核参数选取.计算机应用与软件,2015,32(6):256-259.[doi:10.3969/j.issn.1000-386x.2015.06.063]
15 Kanimozhi T, Latha K. An integrated approach to region based image retrieval using firefly algorithm and support vector machine. Neurocomputing, 2015, 151:1099-1 111.[doi:10.1016/j.neucom.2014.07.078]
16 Ch S, Sohani SK, Kumar D, et al. A support vector machinefirefly algorithm based forecasting model to determine malaria transmission. Neurocomputing, 2014, 129:279-288.[doi:10.1016/j.neucom.2013.09.030]
17王晓静,彭虎,邓长寿,等.基于均匀局部搜索和可变步长的萤火虫算法.计算机应用,2018, 38(3):715-721.
18 Wang H, Cui ZH, Sun H, et al. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Computing, 2017,21(18):5325-5339.[doi:10.1007/s00500-016-2116-z]
19 Yu SH, Zhu SL, Ma Y, et al. A variable step size firefly algorithm for numerical optimization. Applied Mathematics and Computation, 2015, 263:214-220.[doi:10.1016/j.amc.2015.04.065]
20谢金鑫.基于数据挖掘的雾霾天气影响因子研究[硕士学位论文].成都:电子科技大学,2014.
21 Zhang CH, Tian YJ, Deng NY. The new interpretation of support vector machines on statistical learning theory.Science in China Series A:Mathematics, 2010, 53(1):151-164.
22 Fister I, Fister Jr I, Yang XS, et al. A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation,2013. 13:34-46.[doi:10.1016/j.swevo.2013.06.001]
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