Application of the artificial neural network in the forecasting of the airborne contaminant

Release of hazardous materials in chemical industries is a significant threat to surrounding areas. This thread can be answered by the reconstruction system capable of localizing the source of airborne contamination solely based on substance concentrations recorded by the sensors network. However, such systems require multiple runs of the selected atmospheric contaminant transport model. The complexity of the contaminated terrain involves the application of the complicated and computationally expensive dispersion models, while the fast one is too simplified. We examine the possibility of training an artificial neural network (ANN) so that it could effectively simulate the atmospheric toxin transport. The use of a fast neural network in place of costly computational dispersion models in systems localizing the source of contamination might significantly improve their efficiency (speed). In this paper, we train the ANN with the use of the training dataset covering the contamination source term parameters and point output concentrations generated by the Gaussian dispersion model. We test various ANN structures, i.e., numbers of ANN layers, neurons, and activation functions to achieve the ANN capable of estimating the contaminant concentration. Applying the specified ANN topology we train ANN with use of the real field Prairie Grass experiment data. The performed tests confirm that trained ANN has the potential to replace the dispersion model in the contaminant source localization systems.