Papers

Here are some papers which are relatively generic and might be applicable for many ML users in Geoscience.

Gardner, M.W., Dorling, S.R., 1998. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment 32, 2627–2636.

Gevrey, M., Dimopoulos, I., Lek, S., 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, Modelling the structure of aquatic communities: concepts, methods, and problems. 160, 249–264.

Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366.

Kohonen, T., 2001. Self-Organizing Maps, 3rd ed, Springer Series in Information Sciences. Springer-Verlag, Berlin Heidelberg.

Olden, J.D., Jackson, D.A., 2002. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling 154, 135–150 .

Olden, J.D., Joy, M.K., Death, R.G., 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 178, 389–397.

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat, 2019. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195.

Toms, B.A., Barnes, E.A., Ebert-Uphoff, I., 2019. Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability.