Classification of Air Quality Index (AQI) using satellite data and SVM machine learning algorithm

Air pollution is among the most significant environmental challenges on a global scale in the contemporary era, exerting adverse effects on human health and overall well-being. The Air Quality Index (AQI) serves as a metric for the evaluation and communication of air quality, with considerable investments made in the establishment of ground stations for this purpose. The current research introduces a technique for predicting AQI by utilizing satellite data derived from the GEE platform in conjunction with the SVM machine learning model. The SVM algorithm was deployed within this investigation to develop a model for AQI estimation based on the extracted information, with an assessment of validation accuracy against ground-based air quality data. Findings from the study suggest that the proposed model offers a reasonably accurate estimation of AQI and could serve as a monitoring tool for assessing air quality in regions lacking ground-level monitoring facilities. This paper illustrates that the use of satellite data and machine learning models can be an effective tool for air quality monitoring. With further research, the accuracy and reliability of these models can be improved, and they can be used to improve the quality of life for humans worldwide.

Classification of Air Quality Index (AQI) using satellite data and SVM machine learning algorithm Keywords:

Air quality , AQI , SVM , Machine learning algorithm , Google Earth Engine