Deep Learning for Hydrometeorology and Environmental Science

This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality).

Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability a...