作者: Vibha Yadav , Satyendra Nath
DOI: 10.1109/RAETCS.2018.8443887
关键词:
摘要: Due to urbanization and population growths air pollutant (AP) are increasing drastically. Therefore, predictive AP models have become an important tool provide quality management for the site. In this study prediction of PM10 is performed using radial basis function neural network (RBFNN) generalized regression (GRNN). For daily average value PM2.5, NO, Benzene, vertical wind speed Ardali bazar in Varanasi, India considered. RBFNN GRNN incorporates input variables as target variable PM10. It found that predict better than multiple linear models.