pThis 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. ppDeep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.ppMost of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare.pp ppThis book focuses on the explanation of deep learning techniques and their applica