Li Weide, Kong Demeng, Wu Jinran
School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu 730000, China.
North Automatic Control Technology Research Institute, Taiyuan, Shanxi 030006, China.
Comput Intell Neurosci. 2017;2017:2843651. doi: 10.1155/2017/2843651. Epub 2017 Aug 28.
Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.
由于经济的快速增长和城市化的迅速扩张，中国的空气污染问题日益严重，尤其是颗粒物（PM）污染。为了解决日益严重的环境问题，本文利用中国云南省两个重要城市昆明和玉溪在2015年1月1日至2016年8月23日期间的每日PM2.5和PM10浓度数据，提出了一种新的混合模型CI-FPA-SVM来预测空气中PM2.5和PM10的浓度。所提出的模型包括两个部分。首先，由于协整理论在评估不同变量之间可能的相关性方面存在不足，因此引入协整理论来获得输入输出关系，然后利用支持向量机（SVM）得到非线性动力系统，其中参数c和g通过花授粉算法（FPA）进行优化。考虑了六个基准模型，包括FPA-SVM、CI-SVM、CI-GA-SVM、CI-PSO-SVM、CI-FPA-NN和多元线性回归模型，以验证所提出的混合模型的优越性。实证研究结果表明，所提出的CI-FPA-SVM模型具有较高的预测精度，明显优于所有考虑的基准模型，该模型在预测中的应用可以对空气质量进行有效的监测和管理。
