Benzene Prediction: A Comparative Study of ANFIS, LSTM and MLR

It is generally recognized that road traffic emissions are a major health risk and responsible for a substantial share of death and disease in Europe. Although artificial intelligence methods have been used extensively for air pollution forecasting, there is little research on benzene prediction and the use of long short-term memory networks. Benzene is considered one of the pollutants of greatest concern in urban areas and has been linked to leukemia. This paper investigates the predictive power of adaptive neuro-fuzzy inference systems, long short-term memory networks and multiple linear regression models for one hour ahead benzene prediction in the city of Augsburg, Germany. The results of the analysis indicate that adaptive neuro-fuzzy inference systems have the best in sample performance for benzene prediction, whereas long short-term memory networks and multiple linear regressions show similar predictive power. However, long short-term memory models have the best out of sample performance for one hour ahead benzene prediction. This supports the use of long short-term memory networks for benzene prediction in real emission forecasting applications.