Automated neural network forecast of PM_2.5 concentration

[1] Zhao P.S., Dong F., He D., Zhao X.J., Zhang X.L., Zhang W.Z., Yao Q., Liu H.Y., Characteristics of concentrations and chemical compositions for PM_2.5 in the region of Beijing, Tianjin, and Hebei, China, Atmospheric Chemistry and Physics, 13(9), 4631-4644, 2013. Search in Google Scholar

[2] Xing Y.F., Xu Y.H., Shi M.H., Lian Y.X., The impact of PM_2.5 on the human respiratory system, Journal of thoracic disease, 8(1), E69-E74, 2016. Search in Google Scholar

[3] Grantz D.A., Garner J.H.B., Johnson D.W., Ecological effects of particulate matter, Environment international, 29(2-3), 213-239, 2003. Search in Google Scholar

[4] Pérez P., Trier A., Reyes J., Prediction of PM_2.5 concentrations several hours in advance using neural networks in Santiago, Chile, Atmospheric Environment, 34(8), 1189-1196, 2000. Search in Google Scholar

[5] Ordieres J.B., Vergara E.P., Capuz R.S., Salazar R.E., Neural network prediction model for fine particulate matter (PM_2.5) on the USMexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua), Environmental Modelling Software, 20(5), 547-559, 2005. Search in Google Scholar

[6] Zhou Q., Jiang H., Wang J., Zhou J., A hybrid model for PM_2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network, Science of the Total Environment, 496, 264-274, 2014. Search in Google Scholar

[7] Elbayoumi M., Ramli N.A., Yusof N.F.F., Development and comparison of regression models and feedforward back-propagation neural network models to predict seasonal indoor PM_2.5−10 and PM_2.5 concentrations in naturally ventilated schools, Atmospheric Pollution Research, 6(6), 1013-1023, 2015. Search in Google Scholar

[8] Biancofiore F., Busilacchio M., Verdecchia M., Tomassetti B., Aruffo E., Bianco S., Tommaso D.S., Colangeli C., Rosatelli G., Carlo D.P., Recursive neural network model for analysis and forecast of PM₁₀ and PM_2.5, Atmospheric Pollution Research, 8(4), 652-659, 2017. Search in Google Scholar

[9] Chen B., Wang X., Yu L., Wang H., Li Y., Chen J., Chen B., Wang X., Yu L., Wang H., Li Y., Chen J., Zhu J., Nan H., Hou L., Prediction of PM_2.5 concentration in a agricultural park based on BP artificial neural network, Advance Journal of Food Science and Technology, 11(4), 274-280, 2016. Search in Google Scholar

[10] Ong B.T., Sugiura K., Zettsu K., Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM_2.5, Neural Computing and Applications, 27, 1553-1566, 2016. Search in Google Scholar

[11] Jiang P., Dong Q., Li P., A novel hybrid strategy for PM_2.5 concentration analysis and prediction, Journal of environmental management, 196, 443-457, 2017. Search in Google Scholar

[12] Zhang G., Rui X., Fan Y., Critical review of methods to estimate PM_2.5 concentrations within specified research region, ISPRS International Journal of Geo-Information, 7(9), 368, 2018. Search in Google Scholar

[13] Zhang Z., Wang J., Hart J.E., Laden F., Zhao C., Li T., Zheng P., Li D., Ye Z., Chen K., National scale spatiotemporal land-use regression model for PM_2.5, PM₁₀ and NO₂ concentration in China, Atmospheric environment, 192, 48-54, 2018. Search in Google Scholar

[14] Liu H., Duan Z., Chen C., A hybrid framework for forecasting PM_2.5 concentrations using multi-step deterministic and probabilistic strategy, Air Quality Atmosphere and Health, 12, 785-795, 2019. Search in Google Scholar

[15] Doreswamy, Harishkumar K.S., Yogesh K.M., Gad I., Forecasting air pollution particulate matter (PM_2.5) using machine learning regression models, Procedia Computer Science, 171, 2057-2066, 2020. Search in Google Scholar

[16] Kumar S., Mishra S., Singh S.K., A machine learning-based model to estimate PM_2.5 concentration levels in Delhi’s atmosphere, Heliyon, e05618, 2020. Search in Google Scholar

[17] Chang-Hoi H., Park I., Oh H.R., Gim H.J., Hur S.K., Kim J., Choi D.R., Development of a PM_2.5 prediction model using a recurrent neural network algorithm for the Seoul metropolitan area Republic of Korea, Atmospheric Environment, 245, 118021, 2021. Search in Google Scholar

[18] Krogh A., What are artificial neural networks?, Nature biotechnology, 26, 195-197, 2008. Search in Google Scholar

[19] Prasad K.V., Vaidya H., Swamy K.K., Renuka S., Pumpkin Seeds Classification: Artificial Neural Network and Machine Learning Methods, Journal of International Academy of Physical Sciences, 27(1), 22-23, 2023. Search in Google Scholar

[20] Song Y.Y., Lu Y., Decision tree methods: applications for classification and prediction, Shanghai Arch Psychiatry, 27(2), 130, 2015. Search in Google Scholar

[21] Maulud D., Abdulazeez A.M., A review on linear regression comprehensive in machine learning, Journal of Applied Science and Technology Trends, 1(4), 140-147, 2020. Search in Google Scholar

[22] Chicco D., Warrens M.J., Jurman G., The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Computer Science, 7, e623, 2021. Search in Google Scholar