Dự Đoán Mức Độ Bụi PM2.5 Bằng Phương Pháp Khai Phá Dữ Liệu

Abstract

The global air pollution is constantly increasing and causing negative effects on human health such as respiratory, cardiovascular diseases and cancers. Recently, pollution in Hanoi has become increasingly worse , especially when PM2.5 concentration is always at high level. Thus, PM2.5 prediction is of more urgency to issue early forecasts. Depending on air data including meteorological indicators and air pollution indicators collected in Hanoi, we implemented a new characteristic extraction method that gave better results when running the same algorithm compared to those of old methods. XGBoost algorithm was applied to predict the concentration of PM2.5 and the test showed that the accuracy of this algorithm is higher than that of other data mining algorithms during training time, which is significantly lower.