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

Accuracy in the prediction of the particulate matter (PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 and PM10, respectively. The NF-E could improve the efficiency of other models by 4-22% for PM2.5 and 3-20% for PM10 depending on the model.

An emotional artificial neural network for prediction of vehicular traffic noise.

Road traffic is a leading source of environmental noise pollution in large cities, which greatly affects the health and well-being of people. A reliable method for the prediction of road traffic noise is required for monitoring and assessment of traffic noise exposure. This study presents the first application of the Emotional Artificial Neural Network (EANN), as a new generation of neural network method for modeling the road traffic noise in Nicosia, North Cyprus. The efficiency of the EANN model was validated in comparison with the classical feed-forward neural network (FFNN) using two different scenarios with different input combinations. In the first scenario, vehicular classification (the number of cars, medium vehicles, heavy vehicles) and average speed were considered as the models' inputs. In the second scenario, the total traffic and percentage of heavy vehicles were used instead of the classification where the input parameters were total traffic volume, average speed and percentage of heavy vehicles. Application of the EANN model in the prediction of road traffic noise could improve the efficiency of the FFNN, MLR and empirical models at the verification stage up to 14%, 35% and 37%, respectively. Classifying the traffic volume into sub-classes (in scenario 1) before feeding them into the models improved the performance of the EANN and FFNN models at the verification stage by 8% and 12%, respectively. Sensitivity analysis of the input parameters indicated that total traffic volume is the most relevant factor influencing road traffic noise in the study area followed by the number of cars, medium vehicles, heavy vehicles, average speed and percentage of heavy vehicles, respectively.

Artificial intelligence based ensemble model for prediction of vehicular traffic noise.

Vehicular traffic noise is the main source of noise pollution in major cities around the globe. A reliable and accurate method for the estimation of vehicular traffic noise is therefore essential for creating a healthy noise-free environment. In this study, 2 linear (simple average and weighted average) and 2-nonlinear (neural network and neuro-fuzzy) ensemble models were developed by combining the outputs of three Artificial Intelligence (AI) based non-linear models; Adaptive Neuro Fuzzy Inference System (ANFIS), Feed Forward Neural Network (FFNN), Support Vector Regression (SVR) and one Multilinear regression (MLR) model to enhance the performance of the single black box models in predicting vehicular traffic noise of Nicosia city, North Cyprus. In this way, first a nonlinear sensitivity analysis was applied to select the most relevant and dominant input parameters of the traffic data obtained from 12 observation points in the study area. The most dominant parameters in order of their importance were determined to be number of cars, number of van/pickups, number of trucks, average speed and number of buses. Classifying the number of vehicles into five categories before feeding the traffic data into the AI models was observed to improve performance of the single models up to 29% in the verification phase. Out of the four ensembles models developed, the nonlinear ANFIS ensemble was found to be the most robust by improving the performance of ANFIS, FFNN, SVR and MLR models in the verification stage by 11%, 19%, 21% and 31%, respectively.