Prediction of particulate matter PM2.5 level in the air of Islamabad, Pakistan by using machine learning and deep learning approaches

Introduction: Air pollution is a significant global health challenge, contributing to the deaths of millions of people annually. Among these pollutants, Particulate Matter (PM2.5) is the most harmful to the respiratory system causing serious health problems. This study focused on predicting PM2.5 in the air of Islamabad, capital of Pakistan by using machine learning and deep learning models. Materials and methods: Two machine learning models (Decision Tree and Random Forest) and four deep learning models including Multi-Layer Neural Network (MLNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) are used in the study. Each model's performance was assessed by using statistical indicators including coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE). These models are also ranked based on their performance by compromise programming technique. Results: Machine learning models performed better in the training phase by achieving higher R2 values of 0.98 and 0.97 but couldn’t maintain the same performance in the testing phase. Whereas the deep learning models performed best in both the training and testing phases. MLNN model attained higher R2 value of 0.98 in training and 0.88 in testing and is evaluated as top-ranked prediction model in predicting particulate matter PM2.5. Whereas, LSTM, GRU, RNN, Decision Tree, and Random Forest are placed at the 2nd, 3rd, 4th, 5th, and 6th positions having R2 values of 0.86, 0.87, 0.82, 0.99, and 0.97 during training and 0.71, 0.69, 0.69, 0.75, and 0.85 respectively during testing. Conclusion: Deep learning models, especially MLNN, showed strong performance in predicting PM2.5 as compared to the machine learning models.