====== Papers ======

Here are some papers which are relatively generic and might be applicable for many ML users in Geoscience.
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Gardner, M.W., Dorling, S.R., 1998. [[https://doi.org/10.1016/S1352-2310(97)00447-0| 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. [[https://doi.org/10.1016/S0304-3800(02)00257-0|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. [[https://doi.org/10.1016/0893-6080(89)90020-8|Multilayer feedforward networks are universal approximators]]. Neural Networks 2, 359–366. 

Kohonen, T., 2001. [[https://www.springer.com/de/book/9783540679219|Self-Organizing Maps]], 3rd ed, Springer Series in Information Sciences. Springer-Verlag, Berlin Heidelberg.

Olden, J.D., Jackson, D.A., 2002. [[https://doi.org/10.1016/S0304-3800(02)00064-9| 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. [[https://doi.org/10.1016/j.ecolmodel.2004.03.013|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. [[https://doi.org/10.1038/s41586-019-0912-1|Deep learning and process understanding for data-driven Earth system science]]. Nature 566, 195. 

Toms, B.A., Barnes, E.A., Ebert-Uphoff, I., 2019.[[https://arxiv.org/abs/1912.01752 | Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability]].