REAL TIME AIR QUALITY SURVEILLANCE & FORECASTING SYSTEM (RTAQSFS) IN PUNE CITY USING MACHINE LEARNING-BASED PREDICTIVE MODEL

Abstract

Increasing urbanisation and industrialisation produce major environmental challenges such as air pollution, which endangers human, animal, and vegetation life. Reliable measurement, monitoring, and prediction of Air Quality (AQ) have emerged as key global concerns. The State Government-Municipal Corporation is working on policy reforms to fight the deterioration of air quality in Pune and other Indian cities. In this paper, Real Time Air Quality Surveillance & Forecasting System (RTAQSFS) has been developed, which work in the cascaded model incorporating electronics hardware as well as machine learning algorithms. The presence of air pollutants is measured using sensors like MQ135, MQ7, MQ131 etc. The performance of machine learning algorithms viz. Linear regression, Ridge regression, Lasso regression, Decision tree and Random Forest has been evaluated wrt. RMSE and R2. The experimental results show that Random Forest outperforms the other algorithm providing less RMSE and R2 as 99.9% for all the parameters.

Keywords

• air quality
• machine learning
• random forest. ridge
• linear regression
• iot
• sensors