Summary of Hierarchically Disentangled Recurrent Network For Factorizing System Dynamics Of Multi-scale Systems, by Rahul Ghosh et al.
Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems
by Rahul Ghosh, Zac McEachran, Arvind Renganathan, Kelly Lindsay, Somya Sharma, Michael Steinbach, John Nieber, Christopher Duffy, Vipin Kumar
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We present a knowledge-guided machine learning (KGML) framework, dubbed hierarchical recurrent neural architecture, to model multi-scale processes and apply it to streamflow forecasting in hydrology. The proposed framework factorizes system dynamics at multiple temporal scales, capturing their interactions. It consists of an inverse and forward model: the inverse model resolves the system’s temporal modes from data, while the forward model predicts streamflow using these states. This approach enables incorporating new observations without expensive optimization methods, outperforming standard baselines in several river catchments from the NWS NCRFC region. We also demonstrate the effectiveness of training our framework using simulation data or observation data from multiple basins to build a global model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed a new way to predict how much water will flow through rivers and streams, called knowledge-guided machine learning (KGML). This method is good at understanding complex systems that change over time. It’s like trying to predict the weather, but instead of just looking at today’s conditions, it also looks at what happened yesterday, last week, or even last year. The new way of predicting works well even when there isn’t much data available. This can be helpful for places where we don’t have a lot of information about how water flows through the area. |
Keywords
» Artificial intelligence » Machine learning » Optimization
