A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration

Amit Kumar Gorai; Gargi Mitra

doi:10.1007/s11869-016-0417-0

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A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration

Amit Kumar Gorai Gargi Mitra

Year: 2016 Journal: Air Quality Atmosphere & Health Vol: 10 (2)Pages: 213-223 Publisher: Springer Science+Business Media

DOI: 10.1007/s11869-016-0417-0

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Keywords:

Mean squared error Artificial neural network Ozone Environmental science Wind speed Meteorology Troposphere Ground Level Ozone Pollutant Tropospheric ozone Human health Coefficient of determination Relative humidity Humidity Atmospheric sciences Statistics Computer science Mathematics Machine learning Geography Chemistry Geology

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Air Quality Monitoring and Forecasting

Physical Sciences → Environmental Science → Environmental Engineering

Air Quality and Health Impacts

Physical Sciences → Environmental Science → Health, Toxicology and Mutagenesis

Atmospheric chemistry and aerosols

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration

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