PREDICTION OF PM2.5 CONCENTRATIONS USING TEMPERATURE INVERSION EFFECTS BASED ON AN ARTIFICIAL NEURAL NETWORK

11 citations as recorded by crossref.

1. Effects of meteorological parameters and fuel composition on the air pollution production from motor vehicles H. Heydarzadeh et al. 10.1007/s10661-022-09866-0
2. Machine Learning Methods for Statistical Prediction of PM2.5 in Urban Agglomerations with Complex Terrain, Using Grenoble As an Example A. Suslov et al. 10.3103/S0027134924702242
3. A Framework to Predict High-Resolution Spatiotemporal PM2.5 Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China G. Zhang et al. 10.3390/rs12172825
4. Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City G. Asadollahfardi et al. 10.5572/ajae.2016.10.2.067
5. The relative contributions of traffic and non-traffic sources in ultrafine particle formations in Tehran mega city F. Jafarigol et al. 10.1038/s41598-023-49444-z
6. Providing The Classification And Prediction of PM2.5 Pollutant Map Using Machine Learning Methods And Extracting Association Rules M. Heydari et al. 10.61186/jgit.10.4.67
7. A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network Z. Zhang et al. 10.3390/atmos12101327
8. Analysis of Correlation Between Secondary PM2.5 and Factory Pollution Sources by Using ANN and the Correlation Coefficient J. Chang & C. Tseng 10.1109/ACCESS.2017.2765337
9. Prediction of Air Pollutant Concentrations via RANDOM Forest Regressor Coupled with Uncertainty Analysis—A Case Study in Ningxia W. Ding & X. Qie 10.3390/atmos13060960
10. A general review on the applications of machine learning to PM2.5 air pollution forecasting S. Patel et al. 10.1007/s44379-025-00034-y
11. Correlation Between Relative Humidity and Particulate Matter During the Ongoing of Pandemic: A Systematic Review T. Tanatachalert & A. Jumlongkul 10.1007/s41810-023-00186-5