A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration

Accuracy in the prediction of the particulate matter (PM_2.5 and PM₁₀) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM_2.5 and PM₁₀ concentration. The NF-E involves careful selection of relevant input parameters for base modelling and using an adaptive neuro fuzzy inference system (ANFIS) model as a nonlinear kernel for obtaining ensemble output. The four base models used include ANFIS, artificial neural network (ANN), support vector regression (SVR) and multilinear regression (MLR). The dominant input parameters for developing the base models were selected using two nonlinear approaches (mutual information and single-input single-output ANN-based sensitivity analysis) and a conventional Pearson correlation coefficient. The NF-E model was found to predict both PM_2.5 and PM₁₀ with higher generalization ability and least error. The NF-E model outperformed all the single base models and other linear ensemble techniques with a Nash-Sutcliffe efficiency (NSE) of 0.9594 and 0.9865, mean absolute error (MAE) of 1.63 μg/m³ and 1.66 μg/m³ and BIAS of 0.0760 and 0.0340 in the testing stage for PM_2.5 and PM₁₀, respectively. The NF-E could improve the efficiency of other models by 4-22% for PM_2.5 and 3-20% for PM₁₀ depending on the model.