Abstract
Air pollution has a great impact on environment and humans. It is necessary to analyze air pollutant (AP) data but these data are not available in most of the site. Therefore, prediction of AP becomes an important research to solve the problem of time series data. For this, seven ANN models (ANN-1, ANN-2, ANN-3, ANN-4, ANN-5, ANN-6 and ANN-7) with different input variables are developed using Levenberg–Marquadt (LM) algorithm to predict daily A.P. The ANN models are developed using daily measured value of PM10, solar radiation, vertical wind speed, and atmospheric pressure for Ardhali Bazar, Varanasi India. ANN models incorporate 473 data points for training and 31 data points for testing. The results show that vertical wind speed is found to be most influencing variable for PM10 prediction. Apart from this first input combination of solar radiation, wind speed and second input combination of solar radiation, and atmospheric pressure can be used for PM10 prediction.
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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 thanks Central Pollution Control Board, New Delhi India for providing online time series data for this study.
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Yadav, V., Nath, S. (2019). Identification of Relevant Stochastic Input Variables for Prediction of Daily PM10 Using Artificial Neural Networks. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_3
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DOI: https://doi.org/10.1007/978-981-13-0589-4_3
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