Long Short-Term Memory Network for High-Fidelity Tracking of Greenhouse Gas Emission

Abstract

Abstract This paper investigates the use of machine learning to rapidly predict the solutions of a high-fidelity, complex physics model using a simpler physics model. Two different closed-form solutions of the advection-diffusion partial differential equation (A-D PDE), known as the Gaussian plume model and Gaussian puff model, are typically used to model the atmospheric dispersion of gas emission. The Gaussian puff model is a more complex physics-based model that requires more computational effort to generate the high-fidelity solutions, as compared to the simpler Gaussian plume model that has several assumptions and approximations. An encoder-decoder architecture of Long Short-Term Memory (LSTM) network is trained to predict the solutions of the more complex Gaussian puff model using the solutions of the simpler Gaussian plume model for various leak rate, wind speed, and wind direction. The LSTM model, with 3 LSTM layers with 16 neurons each, efficiently simulated the concentrations of the entire set of 2014 samples in a mere 1.34 minutes. This presents a significant contrast to the traditional software's time-consuming simulation process, which took 14 hours to achieve similar concentration outcomes in this study. The implementation of LSTM network has achieved a computational speed up of 625.15 times.