PM2.5 Concentration Prediction Model: A CNN–RF Ensemble Framework

Mei-Hsin Chen, Yao-Chung Chen (), Tien-Yin Chou and Fang-Shii Ning
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Mei-Hsin Chen: GIS Research Center, Feng Chia University, Taichung 40724, Taiwan
Yao-Chung Chen: GIS Research Center, Feng Chia University, Taichung 40724, Taiwan
Tien-Yin Chou: GIS Research Center, Feng Chia University, Taichung 40724, Taiwan
Fang-Shii Ning: Department of Land Economics, National Cheng Chi University, Taipei 11605, Taiwan

IJERPH, 2023, vol. 20, issue 5, 1-13

Abstract: Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 were selected for model training and testing. First, CNN was implemented to extract key meteorological and pollution data. Subsequently, the RF algorithm was employed to train the model with five input factors, namely the extracted features from the CNN and spatiotemporal factors, including the day of the year, the hour of the day, latitude, and longitude. Independent observations from two stations were used to evaluate the models. The findings demonstrated that the proposed CNN–RF model had better modeling capability compared with the independent CNN and RF models: the average improvements in root mean square error (RMSE) and mean absolute error (MAE) ranged from 8.10% to 11.11%, respectively. In addition, the proposed CNN–RF hybrid model has fewer excess residuals at thresholds of 10 μg/m 3 , 20 μg/m 3 , and 30 μg/m 3 . The results revealed that the proposed CNN–RF ensemble framework is a stable, reliable, and accurate method that can generate superior results compared with the single CNN and RF methods. The proposed method could be a valuable reference for readers and may inspire researchers to develop even more effective methods for air pollution modeling. This research has important implications for air pollution research, data analysis, model estimation, and machine learning.

Keywords: PM2.5; convolutional neural network; random forest (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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