Weather Pollution Prediction Based on XGBoost and ARIMA: Evidence from Shanghai

Abstract

As a matter of fact, the weather pollution prediction is a pretty important issue in environment analysis. With the rapid development of machine learning techniques and bigdata analysis, it is available to realize accurate prediction based on the state-of-art models. In recent years, the weather condition is pretty worse in Shanghai. With this in mind, the general situation of air quality in Shanghai was statistically analyzed using air pollution detection data such as API in Shanghai from 2014 to 2023, and the relationship between air quality influencing factors and the air quality index API was investigated based on the construction of the machine learning model and scenarios. With the analysis and the usage of XGBoost and ARIMA, one can get the future trends and prediction. According to the analysis, the impact factors are discussed as well as the current limitations are demonstrated. At the same time, the future prospects are discussed.