Abstract
Air pollution is one of the critical health problems affecting the quality of life, especially in city centers. The air quality index (AQI) is the primary parameter used to measure air pollution. This parameter is constantly measured in city centers with measuring devices that contain various sensors. Due to the high purchasing costs and the need for periodic calibration, there is a need to develop more economical technologies to calculate AQI values. This study aims to predict air pollution with minimum sensors based on machine learning. In the study, daily-based five-year air quality measurement data from India, taken from the Kaggle database, were used. Air quality data includes \(PM_{2.5}\), \(PM_{10}\), \(O_{3}\), \(NO_{2}\), \(SO_{2}\), \(CO_{}\) values obtained from sensors in the measuring devices, and AQI calculated with these values. A feature selection algorithm is used to reduce the sensor cost. Then, artificial intelligence-based AQI was calculated with minimum sensor data. According to the findings, artificial intelligence-based AQI calculation model performances r and RMSE were determined as 0.93 and 20.57, respectively. It has been evaluated that AQI data can be calculated based on artificial intelligence with a minimum of sensors.
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Sari, F.A., Haşıloğlu, M.A., Uçar, M.K., Güler, H. (2023). Determining Air Pollution Level with Machine Learning Algorithms: The Case of India. In: Hemanth, D.J., Yigit, T., Kose, U., Guvenc, U. (eds) 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering. ICAIAME 2022. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-031-31956-3_48
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