Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India.

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and forecasting models for effective air quality monitoring that gather and forecast information about air pollution concentration are essential in every city. Air quality predictions have evolved to be more helpful for management. Recently, better performance and ability have developed due to the involvement of machine learning (ML) and artificial intelligence (AI) in forecasting air quality in urban cities in India. This paper focuses on air pollution as a significant ecological problem that directly impacts human health and the distribution of an environmental system in urban areas. Hence, we have developed advanced models for daily AQI forecasting to understand the air effluence level in the upcoming days. In this research, six data-driven models have been developed and implemented for daily AQI forecasting in the study area; it is crucial for understanding the future air pollution levels to plan and control air pollution in the entire city. The developed model is applied to air quality datasets. A comparison of the performance of ML models tested here indicates that the XGBoost algorithm achieves the highest coefficient of determination (R) and root-mean-square deviation (RMSE) value of 0.99 and lower values value of 4.65 than other models in the testing phase. The results of the artificial neural network (ANN) algorithm are slightly lower than the extreme gradient boosting (XGBoost model); the ANN model results are as R, mean squared error (MSE), and RMSE values of 0.99, 13.99, and 198.88, respectively. All the models were subjected to a ten-fold cross-validation model. However, the RF cross-validation model outperforms other models; the RF model result shows the R, RMSE, and MSE values of 0.99, 3.64, and 4.12, respectively. This study also employed two interpretable models, namely feature importance analysis and Shapley additive explanation (SHAP), to evaluate both the global and local methods in a manner that is independent of specific ML models. The feature importance shows that particle matter (PM) 2.5, PM10, carbon monoxide (CO), and nitrogen oxides (NO) were the most influential variables. The results determined that such novel DL and ML models may improve the accuracy of AQI forecasts and understanding of air pollution, particularly in metropolitan cities.