Adaptive-neuro fuzzy inference trained with PSO for estimating the concentration and severity of sulfur dioxiderelease

The main purpose of this study is to propose a decision support system that deals with the uncertainties in a model of atmospheric dispersion and in meteorological data (speed and direction of wind), which may negatively affect the model accuracy. This later helps the safety agencies in making decisions and allocating necessary materials and human resources to handle potential disastrous events. In order to investigate the aforementioned issues and provide a more reliable data we propose the adaptive Neuro-Fuzzy inference (ANFIS) system enhanced by the mean particle swarm optimization (PSO) to predict the concentration of Sulfur Dioxide release in the atmosphere. This method takes the advantages of fuzzy logic system to address the uncertainties and the ability of neural network to learn from the data. Furthermore our study attempts to estimate the severity index of the released material with the help of fuzzy logic. The result of our study shows that the presented method is successfully applied and it can be a powerful alternative to deal with Sulfur Dioxide release.