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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Erkki Tomppo,Juha Heikkinen,Nina Vainikainenhe number of weights exponentially grows, especially in a deep learning machine. In recent years, several methods updating weights have been developed to improve the speed of convergence and to find the best trajectory to reach the optimum of the employed loss function for a network. In this chapter

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Conscientious

Keith Postlethwaite,Nigel Skinners been developed and applied in a number of fields. Recurrent neural network models can allow forecasting future better, and long short-term memory (LSTM) is a breakthrough to overcome the shortages of the previous RNN model. These algorithms are explained in detail in this chapter.

Goblet-Cells

Debas Senshaw,Hossana Twinomurinziy resources (.). It provides multiple levels of abstractions to choose the right one. The high-level Keras API can be used to build and train models by easily getting started with Tensorflow. Keras allows employing Tensorflow without losing its flexibility and capability. In the following, two appli

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Debas Senshaw,Hossana Twinomurinziology, time-series deep learning models are mainly employed. In this chapter, the development procedure of a time series deep learning model for stochastic simulation producing a long sequence that mimics historical series is explained. Furthermore, the case study for daily maximum temperature with

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https://doi.org/10.1007/978-3-030-64777-3Hydrology; Meteorology; Artificial neural networks; Climate index; Convolutional neural networks; Lon Sho

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978-3-030-64779-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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