A Combined Model Based on Feature Selection and WOA for PM2.5 Concentration Forecasting
School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Center of Data Science, Lanzhou University, Lanzhou 730000, China
Laboratory of Applied Mathematics and Complex System, Lanzhou University, Lanzhou 730000, China
Author to whom correspondence should be addressed.
Received: 22 March 2019 / Revised: 17 April 2019 / Accepted: 17 April 2019 / Published: 24 April 2019
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As people pay more attention to the environment and health,
receives more and more consideration. Establishing a high-precision
concentration prediction model is of great significance for air pollutants monitoring and controlling. This paper proposed a hybrid model based on feature selection and whale optimization algorithm (WOA) for the prediction of
concentration. The proposed model included five modules: data preprocessing module, feature selection module, optimization module, forecasting module and evaluation module. Firstly, signal processing technology CEEMDAN-VMD (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition) is used to decompose, reconstruct, identify and select the main features of
concentration series in data preprocessing module. Then, AutoCorrelation Function (ACF) is used to extract the variables which have relatively large correlation with predictor, so as to select input variables according to the order of correlation coefficients. Finally, Least Squares Support Vector Machine (LSSVM) is applied to predict the hourly
concentration, and the parameters of LSSVM are optimized by WOA. Two experiment studies reveal that the performance of the proposed model is better than benchmark models, such as single LSSVM model with default parameters optimization, single BP neural networks (BPNN), general regression neural network (GRNN) and some other combined models recently reported.
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MDPI and ACS Style
Zhao, F.; Li, W. A Combined Model Based on Feature Selection and WOA for PM2.5 Concentration Forecasting. Atmosphere 2019, 10, 223.
Zhao F, Li W. A Combined Model Based on Feature Selection and WOA for PM2.5 Concentration Forecasting. Atmosphere. 2019; 10(4):223.
Zhao, Fang; Li, Weide. 2019. "A Combined Model Based on Feature Selection and WOA for PM2.5 Concentration Forecasting." Atmosphere 10, no. 4: 223.
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