Daily Prediction of PM10using Radial Basis Function and Generalized Regression Neural Network

机译：径向基函数和广义回归神经网络预测PM 10

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

机译：由于城市化和人口增长，空气污染物（AP）急剧增加。因此，预测性AP模型已成为为站点提供空气质量管理的重要工具。在这项研究中，使用径向基函数神经网络（RBFNN）和广义回归神经网络（GRNN）对PM10进行预测。对于此PM2.5的每日平均值，考虑了印度瓦拉纳西的NO，苯，垂直风速和Ardali Bazar的PM10。 RBFNN和GRNN将输入变量PM2.5，NO，苯，垂直风速和目标变量合并为PM10。结果发现，RBFNN的预测优于GRNN和多元线性回归模型。

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来源
《2018 Recent Advances on Engineering, Technology and Computational Sciences》|2018年|1-5|共5页
会议地点 Allahabad(IN)
作者
Vibha Yadav; Satyendra Nath;
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作者单位

Department of Environmental Sciences and NRM College of Forestry, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, Uttar Pradesh, India;

Department of Environmental Sciences and NRM College of Forestry, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, Uttar Pradesh, India;

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原文格式 PDF
正文语种 eng
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关键词
Atmospheric modeling; Artificial neural networks; Predictive models; Air pollution; Wind speed;

机译：大气建模人工神经网络预测模型空气污染风速;

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Daily Prediction of PM10using Radial Basis Function and Generalized Regression Neural Network

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