Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3341
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dc.contributor.authorSamal, K. Krishna Rani-
dc.contributor.authorBabu, Korra Sathya-
dc.contributor.authorDas, Santosh Kumar-
dc.contributor.authorAcharaya, Abhirup-
dc.date.accessioned2019-08-30T15:22:38Z-
dc.date.available2019-08-30T15:22:38Z-
dc.date.issued2019-08-
dc.identifier.citationInternational Conference on Information Technology and Computer Communications (ITCC 2019), Singapore, 16-18 August 2019en_US
dc.identifier.urihttp://hdl.handle.net/2080/3341-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractAir pollution severely affects many countries around the world causing serious health effects or death. Increasing dependency on fossil fuels through the last century has been responsible for the degradation in our atmospheric condition. Pollution emitting from various vehicles also cause an immense amount of pollution. Pollutants like RSPM, SO2, NO2, SPM, etc. are the major contributors to air pollution which can lead to acute and chronic effects on human health. The research focus of this paper is to identify the usefulness of analytics models to build a system that is capable of giving a rough estimate of the future levels of pollution within a considerable confidence interval. Rendered linear regression techniques are found to be insufficient for the time dependent data. In this regard, we have used time series forecasting approach for predicting the future levels of various pollutants within a considerable confidence interval. The experimental analysis of the forecasting for the air pollution levels of Bhubaneswar City indicates the effectiveness of our proposed method using SARIMA and Prophet model.en_US
dc.subjectPollutionen_US
dc.subjectTime Seriesen_US
dc.subjectSARIMA modelen_US
dc.subjectProphet modelen_US
dc.subjectSO2en_US
dc.subjectNO2en_US
dc.subjectRSPMen_US
dc.subjectSPMen_US
dc.titleTime Series based Air Pollution Forecasting using SARIMA and Prophet Modelen_US
dc.typeArticleen_US
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