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Neural Network Modeling and Prediction of Daily Average Concentrations of PM\(_{10}\), NO\(_{2}\) and SO\(_{2}\)

  • Sateesh N. HosamaneEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

Three-layer principal component based artificial neural network (ANN) model is used to predict PM\(_{10}\), NO\(_{2}\) and SO\(_{2}\) concentration. The developed model predictions are compared with the measured pollutant concentrations. The daily average pollutant concentrations and five meteorological variables are used to develop pollution forecast models. The selected monitoring site is a typical residential area with high traffic influence and the air pollution is because of nearby industries. A principal component regression (PCR) model is used for comparing the results obtained by the developed neural network model. The performance of the developed model were assessed using various performance index. Developed models exhibit a decent performance >70–95% for three measured pollutants. The future models performed with good accuracy and the predicted pollutant concentrations were confirmed to be adequate after computing the accuracy using performance indicators.

Keywords

Pollutants Meteorological parameters PCA PCR ANN 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Chemical EngineeringKLE Dr. M.S. Sheshgiri College of Engineering and TechnologyBelgaumIndia

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