Abstract
In this chapter, a summary of the material introduced in the book is presented. Advantages and limitations of neural network (NN) techniques are discussed. Some other statistical learning techniques, such as the nearest neighbor approximation, the regression tree, and the Random Forest approximation, are briefly discussed. Their performances are compared with the performance of the NN emulation for the case when these techniques are applied to emulate a long wave radiation parameterization in an atmospheric model. The chapter contains a list of references giving background and further detail to the interested reader on each examined topic.
Science is facts. Just as houses are made of stones, so science is made of facts. But a pile of stones is not a house and a collection of facts is not necessarily a science.
– Jules Henri Poincare, Science and Hypothesis
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Krasnopolsky, V.M. (2013). Conclusions. In: The Application of Neural Networks in the Earth System Sciences. Atmospheric and Oceanographic Sciences Library, vol 46. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6073-8_6
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DOI: https://doi.org/10.1007/978-94-007-6073-8_6
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