EESS talk on "How to beat your teachers in hydrologic machine learning"
Event details
| Date | 26.10.2021 |
| Hour | 12:15 › 13:15 |
| Speaker | Dr Chaopeng Shen, Associate Professor, visiting professor at EAWAG, Civil and Environmental Engineering, College of Engineering, Pennsylvania State University (US) |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract:
Deep learning (DL) models trained on hydrologic observations are recently shown to be highly performant. Used directly, however, they inherit certain flaws of their supervising data. In other words, these models are students that cannot exceed their teachers (supervising data). For example, satellite data has global coverage but low resolution/accuracy, while in-situ networks are spatially imbalanced. For another example, we cannot predict a variable at large scales if we do not have extensive observations for it and observational noise propagates into the trained models. While some have shown that adding physical constraints could be beneficial, the benefit has so far been limited to minor-to-modest gains in performance. Here we explore several pathways for machine learning models to exceed their teachers. First we explore learning from multiple data sources (soil moisture) at different scales, creating a multiscale forecast model that breaks the confines of the individual supervising dataset. Second we demonstrate how we connect machine learning with physics-based models to predict unobserved variables that help determine future trends of the water cycle. Third we show how network models can be leveraged to learn physics rather than purely making predictions. Overall, there are substantial new paths to take for hydrology to benefit from big data machine learning apart from elevating the prediction accuracy.
Short biography:
Chaopeng Shen is Associate Professor in Civil Engineering at The Pennsylvania State University and is currently visiting Swiss Federal Institute of Aquatic Science and Technology (EAWAG) as an academic guest. He received the Ph.D. degree in environmental engineering from Michigan State University, East Lansing, MI, USA, in 2009. His PhD research focused on computational hydrology and he developed the hydrologic model Process-based Adaptive Watershed Simulator(PAWS), which was later coupled to the community land model to study the interactions between hydrology and ecosystem. His recent efforts focused on harnessing the big data and machine learning opportunities in advancing hydrologic predictions and connecting physics with machine learning. He has written technical, editorial, review and collective opinion papers on hydrologic deep learning to call to attention the emerging opportunities for scientific advances. In addition, his research interests also include floodplain systems, scaling issues, process-based hydrologic modeling, and hydrologic data mining. He is currently an Associate Editor of the Water Resources Research and also an Associate Editor in Frontiers in AI.
Deep learning (DL) models trained on hydrologic observations are recently shown to be highly performant. Used directly, however, they inherit certain flaws of their supervising data. In other words, these models are students that cannot exceed their teachers (supervising data). For example, satellite data has global coverage but low resolution/accuracy, while in-situ networks are spatially imbalanced. For another example, we cannot predict a variable at large scales if we do not have extensive observations for it and observational noise propagates into the trained models. While some have shown that adding physical constraints could be beneficial, the benefit has so far been limited to minor-to-modest gains in performance. Here we explore several pathways for machine learning models to exceed their teachers. First we explore learning from multiple data sources (soil moisture) at different scales, creating a multiscale forecast model that breaks the confines of the individual supervising dataset. Second we demonstrate how we connect machine learning with physics-based models to predict unobserved variables that help determine future trends of the water cycle. Third we show how network models can be leveraged to learn physics rather than purely making predictions. Overall, there are substantial new paths to take for hydrology to benefit from big data machine learning apart from elevating the prediction accuracy.
Short biography:
Chaopeng Shen is Associate Professor in Civil Engineering at The Pennsylvania State University and is currently visiting Swiss Federal Institute of Aquatic Science and Technology (EAWAG) as an academic guest. He received the Ph.D. degree in environmental engineering from Michigan State University, East Lansing, MI, USA, in 2009. His PhD research focused on computational hydrology and he developed the hydrologic model Process-based Adaptive Watershed Simulator(PAWS), which was later coupled to the community land model to study the interactions between hydrology and ecosystem. His recent efforts focused on harnessing the big data and machine learning opportunities in advancing hydrologic predictions and connecting physics with machine learning. He has written technical, editorial, review and collective opinion papers on hydrologic deep learning to call to attention the emerging opportunities for scientific advances. In addition, his research interests also include floodplain systems, scaling issues, process-based hydrologic modeling, and hydrologic data mining. He is currently an Associate Editor of the Water Resources Research and also an Associate Editor in Frontiers in AI.
Practical information
- General public
- Free
- This event is internal
Organizer
- EESS - IIE
Contact
- Prof. Devis Tuia, ECEO