Abstract
In this paper, short-term forecasting of nitrogen dioxide (NO2) at one day ahead is performed using nonlinear autoregressive neural network. For this, 491 measured time series data are utilized. The presented results with root mean square error of 0.0456 validate accuracy and effectiveness of the proposed nonlinear autoregressive neural network forecasting model for NO2.
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Acknowledgements
The authors would like to acknowledge Department of Science and Technology, New Delhi-110016, India for providing inspire fellowship with Ref. No. DST/INSPIRE Fellowship/2016/IF160676. We would also like to thank Central Pollution Control Board, New Delhi India for providing online time series data for this study.
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Yadav, V., Nath, S., Malik, H. (2019). Forecasting of Nitrogen Dioxide at One Day Ahead Using Nonlinear Autoregressive Neural Network for Environmental Applications. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-13-1819-1_58
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DOI: https://doi.org/10.1007/978-981-13-1819-1_58
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