Forecasting air quality index using regression models: A case study on Delhi and Houston

• Published in: 2017 International Conference on Trends in Electronics and Informatics (ICEI) pp. 248 - 254
• Main Authors: Ganesh, S Sankar, Modali, Sri Harsha, Palreddy, Soumith Reddy, Arulmozhivarman, P
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2017
• Subjects: Air quality; Air quality index; Atmospheric modeling; Data models; forecasting; gradient descent; Indexes; regression; Stochastic processes; support vector machine; Support vector machines; Testing
• DOI: 10.1109/ICOEI.2017.8300926

It is always important to monitor the quality of air that we inhale to protect ourselves from the respiratory diseases. In this paper, we present different regression models to forecast air quality index (AQI) in particular areas of interest. Support vector regression (SVR) and linear models like multiple linear regression consisting of gradient descent, stochastic gradient descent, mini-batch gradient descent were implemented. In these models, the air quality index (AQI) is dependent on pollutant concentrations of NO 2 , CO, O 3 , PM 2.5 , PM 10 and SO 2 . Among these models, support vector regression (SVR) exhibited high performance in terms of investigated measures of quality.