Abstract
Before applying a neural network model, the data must be preprocessed in advance. Data normalization must be made to avoid the difference of data variability for inputs and outputs. It also simplifies the parameter range of a network model. Furthermore, data should be split for different purposes such as training, validation and testing. In this chapter, these data normalization and data split are explained in detail.
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Lee, T., Singh, V.P., Cho, K.H. (2021). Data Preprocessing. In: Deep Learning for Hydrometeorology and Environmental Science. Water Science and Technology Library, vol 99. Springer, Cham. https://doi.org/10.1007/978-3-030-64777-3_3
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DOI: https://doi.org/10.1007/978-3-030-64777-3_3
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Publisher Name: Springer, Cham
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