Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 8 Issue 9
Air Quality Prediction using Machine Learning Algorithms
Pooja Bhalgat, Sejal Pitale, Sachin Bhoite
10.7753/IJCATR0809.1006
keywords : Machine Learning, Time Series, Prediction, Air Quality, SO2
Examining and protecting air quality has become one of the most essential activities for the government in many industrial and urban areas today. The meteorological and traffic factors, burning of fossil fuels, and industrial parameters play significant roles in air pollution.With this increasing air pollution,Weare in need of implementing models which will record information about concentrations of air pollutants(so2,no2,etc).The deposition of this harmful gases in the air is affecting the quality of people’s lives, especially in urban areas. Lately, many researchers began to use Big Data Analytics approach as there are environmental sensing networks and sensor data available.In this paper, machine learning techniques are used to predict the concentration of so2 in the environment. Sulphur dioxide irritates the skin and mucous membranes of the eyes, nose, throat, and lungs.Models in time series are employed to predict the so2 readings in nearing years or months.
@artical{p892019ijcatr08091006,
Title = "Air Quality Prediction using Machine Learning Algorithms",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="9",
Pages ="367 - 370",
Year = "2019",
Authors ="Pooja Bhalgat, Sejal Pitale, Sachin Bhoite"}
Researcher used AR(autoregressive) model for forecasting.
The model achieved an overall MSE of 166.358.
This is a proposed system build using time series algorithms which will be predicting SO2 values year wise based on past values.
We used 70% of the data for training predictive model and 30% for testing.