文章基本信息

标题：Effects of Chelating Surfactants on Competitive Adsorption of Lead and Zinc on Loess Soil
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作者：P.Vinay Kumar ; M.C.Ajay Kumar ; B.Anil Kumar 等
期刊名称：Nature, Environment and Pollution Technology
印刷版ISSN：0972-6268
出版年度：2022
卷号：21
期号：2
页码：691-696
DOI：10.46488/NEPT.2022.v21i02.029
语种：English
出版社：Technoscience Publications
摘要：Urbanization and Industrialization during the last few decades have increased air pollution causing harm to human health. Air pollution in metro cities turns out to be a serious environmental problem, especially in developing countries like India. The major environmental challenge is, to predict accurate air quality from pollutants. Envisaging air quality from pollutants like PM2.5, using the latest deep learning technique (LSTM timer series) has turned out to be a significant research area. The primary goal of this research paper is to forecast near-time pollution using the LSTM time series multivariate regression technique. The air quality data from Central Pollution Control Board over Hyderabad station has been used for the present study. All the processing is done in real-time and the system is found to be functionally very stable and works under all conditions. The Root Mean Square Error (RMSE) and R2 have been used as evaluation criteria for this regression technique. Further, the time series regression has been used to find the best fit model in terms of processing time to get the lowest error rate. The statistical model based on machine learning established a relevant prediction of PM2.5 concentrations from meteorological data.
关键词：PM2.5;Air pollution;Deep learning;Forecasting