Noise is considered one of the most critical environmental issues because it endangers the health of living organisms. For this reason, up-to-date knowledge seeks to find the causes of noise in various industries and thus prevent it as much as possible. Considering the development of railway lines in underdeveloped countries, identifying and modeling the causes of vibrations and noise of rail transportation is of particular importance. The evaluation of railway performance cannot be imagined without measuring and managing noise. This study tried to model the maximum A-weighted noise pressure level with the information obtained from field measurements by Emotional artificial neural network (EANN) models and compare the results with linear and logarithmic regression models. The results showed the high efficiency of EANN models in noise prediction so that the prediction accuracy of 95.6% was reported. The results also showed that in noise prediction based on the neural network-based model, the independent variables of train speed and distance from the center of the route are essential in predicting.
