Prediction of Air Quality Index in Colombo

Abstract

Air is always considered as the main critical factor on which human survival depends on. The AQI or long firmly air quality index is the index value that illustrates qualitatively the current state of the air. The substantial AQI will further menace the living creatures’ health & the living atmosphere. Terrible air quality has been a major concern in Sri Lanka, particularly in urban cities such as Colombo and Kandy. Reliable AQI prediction will assist to decrease the health risks caused by air pollution. The goal of this study has been to find the most suitable machine learning approach for predicting accurate air quality index in Colombo based upon PM2.5 particular concentration. In this study, PM2.5 concentration in Colombo had been predicted using four correlated air pollutant concentrations such as SO2, NO2, PM2.5, & PM10. The obtained dataset was pre-processed via prediction in order to improve prediction accuracy. The gathered dataset Cross-validated as according to 80% for training & 20% for testing the prediction model. Machine learning methods such as K-Nearest Neighboring, Multiple Linear-Regression, Random Forest, and Support Vector Machines were used to train and evaluate the prediction models. In the end, we achieved 83.25% accuracy for the K-Nearest Neighboring algorithm model, 84.68% accuracy for the Support Vector Machines model, 85.17% accuracy for the Random Forest model, and 41.9% accuracy for the Multiple Regression Model. Random Forest was recognized as the best appropriate prediction model after evaluating the models, with over 85% greater accuracy.