Air quality forecasting and rating based on machine learning algorithm and cumulative logit model: an empirical study for Lanzhou city of China
Ting Xu, Yuzhu Tian, Xinran Cai, C.H. Wu, Zhibao Mian
Abstract
Abstract With the quick development of society and industry, air quality has become a grim and global environmental concern. Predicting and rating air quality for many cities remains a significant challenge. Consequently, machine learning algorithms have garnered considerable attention for their potential to address these issues effectively. In this paper, firstly, based on daily air quality data from July 1, 2022 to June 30, 2023 in Lanzhou city of China, five machine learning models, including Bayes Model Averaging (BMA), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are developed to predict the Air Quality Index (AQI) via six major air pollutants (PM2.5, PM10, SO2, NO2, O3 and CO). Secondly, we integrate Bootstrap algorithm into the optimal model, leading to the proposal of the LSTM-Bootstrap algorithm for deriving the standard errors and confidence intervals of the predicted AQI. Thirdly, a cumulative logit model is employed to evaluate and forecast AQI rating. The analysis results indicate that AQI rating is significantly affected by PM10, CO and O3. Additionally, to validate the efficacy of the suggested methods, a similar analysis is conducted on air quality data from Chengdu city for the same period. The findings provide valuable insights for future environmental policies and air quality management strategies.