A novel approach for air quality prediction using machine learning approach

V.P.S.Sruthi G; Lokesh M; Grace A; L.Reddy S; Srinivas Raja  B

G.V.P.S.Sruthi Department of Electronics and Communication Engineering, Godavari Institute of Engineering and Technology,AndhraPradesh,India
M.Lokesh Department of Electronics and Communication Engineering, Godavari Institute of Engineering and Technology,AndhraPradesh,India
A.Grace Department of Electronics and Communication Engineering, Godavari Institute of Engineering and Technology,AndhraPradesh,India
S.L.Reddy Department of Electronics and Communication Engineering, Godavari Institute of Engineering and Technology,AndhraPradesh,India
B.Srinivas Raja Department of Electronics and Communication Engineering, Godavari Institute of Engineering and Technology,AndhraPradesh,India,

Abstract

We forecast the air quality by using machine learning to predict the air quality index of a given area. Air quality index of India is a standard measure used to indicate the pollutant (so2, no2, rspm, spm. etc.) levels over a period. We developed a model to predict the air quality index based on historical data of previous years and predicting over a particular upcoming year using machine learning methods.Our model will be capable for successfully predicting the air quality index of any bounded region provided with the historical data of pollutant concentration.In our model by implementing the proposed parameter reducing formulations, we achieved better performance than the standard regression models.This project work can help in constructing air quality, using machine learning methods such as XGBoost, Random Forest (RF) and Convolution Neural Network (CNN).Among these methods, Convolution Neural Network produces a better result in terms of accuracy value of about 91% compared to other algorithms.

Keywords: Air Quality Index , rspm, spm, Pollutant , Machine Learning.

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