Titlebook: Deep Learning for Hydrometeorology and Environmental Science; Taesam Lee,Vijay P. Singh,Kyung Hwa Cho Book 2021 The Editor(s) (if applicab

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彩色的蜡笔

注意

OMIT

Deep Learning for Spatial Datasets,Among recent developments of deep learning models, the availability of spatial datasets or images with deep learning is the most significant contribution. In this chapter, a convolutional neural network (CNN) that can analyze the spatial datasets is described. The training procedure of CNN is described with a simple example.

细丝

Environmental Applications of Deep Learning,A case study of CNN, a spatial deep learning model, is presented in this chapter for analyzing water quality with remote sensing data. Airborne remote sensing of cyanobacteria with multispectral/hyperspectral sensors is employed for input data and its water quality as output data.

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ACME

https://doi.org/10.1007/978-3-319-66387-6esented, including the definition and pros and cons of deep learning, followed by the recent applications of deep learning models in hydrological and environmental fields. The structure of the remaining chapters for this book is also explained.

臭了生气

https://doi.org/10.1007/978-3-319-66387-6are explained, including the definition of error terms and parameter estimation procedure, since they are similarly used in deep learning models. Also, the basic concept of the time series model is also explained and this part is mainly referred to in the LSTM model chapter.

伪造

The Art or Science of Manipulation. The simplest neural network model is introduced and used in the latter part of this book. Then, a full neural network model is described. The parameter estimation procedure employing backward propagation is also explained.

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