A comparison of machine learning-based approaches in estimating surface PM2.5 concentrations focusing on Artificial Neural Networks and high pollution events

This study compares various machine learning models, including linear models, tree-based algorithms, and artificial neural networks (ANNs) for estimating PM2.5 concentrations using the MERRA-2 dataset from 2012 to 2023. Mutual information and Spearman cross-feature correlation scores are used during feature selections.

The performance of models is evaluated using metrics including normalized Nash–Sutcliffe efficiency (NNSE), root mean standard deviation ratio (RSR), and mean percentage error (MPE). Our results show that ANNs outperform linear and tree models, particularly in estimating daily PM2.5 concentrations of 35–1000 µg/m3. ANNs improve NNSE by 119% and 46%, RSR by 40% and 24%, and MPE by 44% and 30% from linear and tree models, respectively, indicating ANN’s superior estimation performance during high pollution days.