Use deep convolutional neural nets to learn patterns of mismatch between a land surface model and GRACE satellite
2019 Spring Cyberseminar Series
- Alex Sun / University of Texas at Austin
Global hydrological models are increasingly being used to simulate spatial and temporal patterns of total water storage. Missing physical processes and/or uncertain parameterization in these models may introduce significant uncertainty in model predictions. GRACE satellite senses total water storage at the regional and continental scales. In this study, we applied deep convolutional neural nets to learn the spatial and temporal patterns of mismatch between model simulations and GRACE observations. This physically based learning approach leverages strengths of data science and hypothesis-driven process-level models. We show, through three different types of convolution neural network-based deep learning models, that deep learning is a viable approach for improving model-GRACE match. After the deep learning model is trained, GRACE data is not required. As a result, the method can also be used to fill in data gaps between GRACE missions or even before the GRACE mission
2019 Spring Cyberseminar Series: Recent advances in big data machine learning in Hydrology
Hosted by Chaopeng Shen, Pennsylvania State University
Recently big data machine learning has led to substantial changes across many areas of study. In Hydrology, the introduction of big data and machine learning methods have substantially improved our ability to address existing challenges and encouraged novel perspectives and new applications. These advances present new opportunities methods that aid scientific discovery, data discovery, and predictive modeling. This series cover new techniques and findings that have emerged in Hydrology during the previous year, with a focus on catchment and land surface hydrology.