Air Quality—Meteorology Correlation Modeling Using Random Forest and Neural Network

Ruifang Liu (), Lixia Pang, Yidian Yang, Yuxing Gao, Bei Gao, Feng Liu and Li Wang
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Ruifang Liu: Xi’an Meteorological Observatory, Xi’an 710016, China
Lixia Pang: Nanjing University of Information Science and Technology, Nanjing 210014, China
Yidian Yang: Xi’an Meteorological Observatory, Xi’an 710016, China
Yuxing Gao: Xi’an Meteorological Observatory, Xi’an 710016, China
Bei Gao: Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi’an 710014, China
Feng Liu: Xi’an Meteorological Observatory, Xi’an 710016, China
Li Wang: Xi’an Meteorological Observatory, Xi’an 710016, China

Sustainability, 2023, vol. 15, issue 5, 1-22

Abstract: Under the global warming trend, the diffusion of air pollutants has intensified, causing extremely serious environmental problems. In order to improve the air quality–meteorology correlation model’s prediction accuracy, this work focuses on the management strategy of the environmental ecosystem under the Artificial Intelligence (AI) algorithm and explores the correlation between air quality and meteorology. Xi’an city is selected as an example. Then, the theoretical knowledge is explained for Random Forest (RF), Backpropagation Neural Network (BPNN), and Genetic Algorithm (GA) in AI. Finally, GA is used to optimize and predict the weights and thresholds of the BPNN. Further, a fusion model of RF + BP + GA is proposed to predict the air quality and meteorology correlation. The proposed air quality–meteorology correlation model is applied to forest ecosystem management. Experimental analysis reveals that average temperature positively correlates with Air Quality Index ( AQI ), while relative humidity and wind speed negatively correlate with AQI . Moreover, the proposed RF + BP + GA model’s prediction error for AQI is not more than 0.32, showing an excellently fitting effect with the actual value. The air-quality prediction effect of the meteorological correlation model using RF is slightly lower than the real measured value. The prediction effect of the BP–GA model is slightly higher than the real measured value. The prediction effect of the air quality–meteorology correlation model combining RF and BP–GA is the closest to the real measured value. It shows that the air quality–meteorology correlation model using the fusion model of RF and BP–GA can predict AQI with the utmost accuracy. This work provides a research reference regarding the AQI value of the correlation model of air quality and meteorology and provides data support for the analysis of air quality problems.

Keywords: random forest; BP neural network model; genetic algorithm; air quality; meteorological correlation; forest ecological management; artificial intelligence; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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