Daily PM2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm
Daily PM2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm
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DOI：
10.1016/j.jenvman.2016.12.011
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发表时间：
2017-03-01
影响因子：
8.7
通讯作者：
Sun, Jingyi
中科院分区：
文献类型：
--
作者：
Sun, Wei;Sun, Jingyi
对中国PM2.5污染的关注越来越大。由于其对环境和健康的有害影响，重要的是建立一个PM2.5浓度预测模型，其监视和控制精度很高。本文提出了一种基于主成分分析（PCA）的新型混合模型，最小二乘支持向量机（LSSVM），由杜鹃搜索（CS）优化。采用第一个PCA来提取原始功能并减少输入选择的尺寸。然后将LSSVM应用于预测每日PM2.5浓度。 LSSVM中的参数通过CS进行了微调，以改善其概括。一项实验研究表明，所提出的方法的表现优于具有默认参数的单个LSSVM模型和PM2.5浓度预测中的一般回归神经网络（GRNN）模型。因此，已建立的模型赋予了应用于空气质量预测系统的潜力。 （c）2016 Elsevier Ltd.保留所有权利。
Increased attention has been paid to PM2.5 pollution in China. Due to its detrimental effects on environment and health, it is important to establish a PM2.5 concentration forecasting model with high precision for its monitoring and controlling. This paper presents a novel hybrid model based on principal component analysis (PCA) and least squares support vector machine (LSSVM) optimized by cuckoo search (CS). First PCA is adopted to extract original features and reduce dimension for input selection. Then LSSVM is applied to predict the daily PM2.5 concentration. The parameters in LSSVM are fine-tuned by CS to improve its generalization. An experiment study reveals that the proposed approach outperforms a single LSSVM model with default parameters and a general regression neural network (GRNN) model in PM2.5 concentration prediction. Therefore the established model presents the potential to be applied to air quality forecasting systems. (C) 2016 Elsevier Ltd. All rights reserved.