A novel machine learning-based artificial intelligence method for predicting the air pollution index PM2.5

Zhao, Lingxiao; Li, Zhiyang; Qu, Leilei

doi:10.1016/j.jclepro.2024.143042

Journal of Cleaner Production

2024

DOI: 10.1016/j.jclepro.2024.143042

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A novel machine learning-based artificial intelligence method for predicting the air pollution index PM2.5

Lingxiao Zhao,

Zhiyang Li,

Leilei Qu

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2024

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Cited by 3 publications

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Applications of machine learning in drinking water quality management: A critical review on water distribution system

Li,

Ma,

Zhong

et al. 2024

Journal of Cleaner Production

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Applications of machine learning in drinking water quality management: A critical review on water distribution system

Li,

Ma,

Zhong

et al. 2024

Journal of Cleaner Production

View full text Add to dashboard Cite

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Wu,

Lu,

Tian

et al. 2024

Atmosphere

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Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research.

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Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

He,

Guo

2024

Atmosphere

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Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m3. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control.

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A novel machine learning-based artificial intelligence method for predicting the air pollution index PM2.5

Cited by 3 publications

References 59 publications

Applications of machine learning in drinking water quality management: A critical review on water distribution system

Applications of machine learning in drinking water quality management: A critical review on water distribution system

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

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