6224912 – Deep Learning in Hydrological Modeling

Deep Learning in Hydrological (environmental) Modeling is an interdisciplinary course that aims to provide students (Master and PhD students) with a solid foundation in applying advanced deep learning techniques to tackle complex environmental modeling problems. The course will explore the role of deep learning in simulating, predicting, and understanding environmental processes and systems. Students will learn to develop and implement state of the art deep learning models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, to analyze large-scale environmental data sets. These models will be applied to a wide range of environmental applications. Throughout the course, students will acquire practical experience in designing and training deep learning models using the popular frameworks PyTorch. They will gain a deep understanding of the underlying principles and algorithms, such as backpropagation, optimization techniques, and model regularization. In addition to hands-on programming assignments, the course will involve critical analysis of relevant research papers and case studies. Upon completion of this course, students will be equipped with the knowledge and skills necessary to effectively apply deep learning techniques in environmental modeling tasks and contribute to innovative solutions for pressing environmental challenges.