Neural-estimator for the surface emission rate of atmospheric gases

The emission rate of minority atmospheric gases is inferred by a new approach based on neural networks. The new 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 solutions is performed. The results obtained from the neural networks are significantly better. In addition, the inversion with the neural networks is faster than regularized approaches, after training.