Analysis and Prediction for Air Quality Using Various Machine Learning Models
To-Hieu Dao, Hoang Van Nhat, Hoang Quang Trung, Vu Hoang Dieu, Nguyen Thi Thu, Duc-Nghia Tran, Duc-Tan Tran
Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 89–94 (2022)
Abstract. Air pollution has been a concern in recent years. Measuring the extent of pollution is important to know about the air quality. Previous research has used machine learning algorithms to forecast the Air Quality Index (AQI) in specific locations. Even though that research achieved quite reliable results, they still have some drawbacks that need to be taken into consideration, such as low accuracy or lack of data analysis.On a public dataset, we used Random Forest, XGBoost, and Neural Network to build a machine learning model for the purpose of making predictions about the air quality index (AQI) in a number of cities located in India. The performances of these models were evaluated by using their score errors, Root Mean Square Error (RMSE), and Coefficient Of Determination ($R^2$). This paper demonstrates the analysis of air pollutants from the dataset, which is an effective way to enhance the model's performance.
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