Neural-estimator for the surface emission rate of atmospheric gases

TL;DR

This paper introduces a neural network-based method for estimating the surface emission rates of atmospheric gases, demonstrating improved accuracy and speed over traditional inverse solutions.

Contribution

It presents a novel neural network approach, specifically using multi-layer perceptrons, for solving the inverse problem of atmospheric gas emission estimation.

Findings

Neural network approach outperforms regularized inverse solutions in accuracy.

Neural networks provide faster inversion after training.

Significant improvement in emission rate estimation results.

Abstract

The emission rate of minority atmospheric gases is inferred by a new approach based on neural networks. The neural network applied is the multi-layer perceptron with backpropagation algorithm for learning. The identification of these surface fluxes is an inverse problem. A comparison between the new neural-inversion and regularized inverse solution id performed. The results obtained from the neural networks are significantly better. In addition, the inversion with the neural netwroks is fster than regularized approaches, after training.