Efficient Simulation of Pollutant Dispersion Using Machine Learning

An efficient and accurate system capable of predicting contaminant source location is important for environmental monitoring and security systems. However, one of the main challenges in developing such a system is the high computational cost associated with modeling the physical aspects of atmospheric dispersion. The present work addresses this issue by proposing a faster solution that uses a Multi Layer Perceptron (MLP) Neural Network to predict sensor readings based on the source location coordinates. The MLP is trained using synthetic data generated by solving the advection diffusion equation numerically, and collected at predetermined sensor locations and time. The proposed method generates accurate predictions in a fraction of the time required for the conventional method. This approach can help in detecting and identifying contaminants quickly and efficiently, and it has potential applications in environmental monitoring and security systems, making it an important tool for protecting public health and safety.