Air Pollution Level Prediction System
Rajeev Tiwari1, Shuchi Upadhyay2, Parv Singhal3, Ujla Garg4, Shefali Bisht5
1Rajeev Tiwari, Department of Virtualization, System Certification Services, University of Petroleum And Energy Studies, Dehradun, India.
2Shuchi Upadhyay, Department of Virtualization, System Certification Services, University of Petroleum And Energy Studies, Dehradun, India.
3Parv Singhal, Department of Virtualization, System Certification Services, University of Petroleum And Energy Studies, Dehradun, India.
4Ujla Garg, Department of Virtualization, System Certification Services, University of Petroleum And Energy Studies, Dehradun, India.
5Shefali Bisht, Department of Virtualization, System Certification Services, University of Petroleum And Energy Studies, Dehradun, India.
Manuscript received on 03 April 2019 | Revised Manuscript received on 10 April 2019 | Manuscript Published on 13 April 2019 | PP: 1-8 | Volume-8 Issue-6C April 2019 | Retrieval Number: F12100486C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, the levels of air pollutants in the environment are increasing manifold. This has led to deterioration of human lifestyle. Various methods like ‘Climatology’ (based on the assumption that the past is a good predictor of the future) have been used for air quality forecasting. These approaches are usually used to predict exceeding limits from specific thresholds, not ambient concentrations. As a result, a lot of improvement is still required in this field for prediction analysis. With incomplete data parameters and their significance (priority), most of the methods fail to predict the pollution levels significantly. The advantage of artificial neural networks includes the problem-solving efficiency in the cases of unavailability of complete information, with no information about the analytical relationship among the input and processed output data. The aim is to develop an artificial neural network for air quality prediction that can perform with constrained dataset with highly robust feature in order to handle the data including noise and errors. Dataset used deals with pollution in the U.S. involving four major pollutants (Nitrogen Dioxide, Sulphur Dioxide, Carbon Monoxide and Ozone) on daily basis for the time period of year 2008 to 2017. We use prediction models like ARIMA etc. to validate our predicted AQI. This AQI analysis helps in telling the status of present air pollution and forecasted pollution levels in coming time. So, it plays a vital role for decision maker and for individual also to know about air pollution quality.
Keywords: Artificial Neural Network; Environmental Engineering; Air Quality index; ARIMA; Forecasting.
Scope of the Article: Software Defined Networking and Network Function Virtualization