Summary of Streamflow Prediction with Uncertainty Quantification For Water Management: a Constrained Reasoning and Learning Approach, by Mohammed Amine Gharsallaoui et al.
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach
by Mohammed Amine Gharsallaoui, Bhupinderjeet Singh, Supriya Savalkar, Aryan Deshwal, Yan Yan, Ananth Kalyanaraman, Kirti Rajagopalan, Janardhan Rao Doppa
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach is proposed in this paper, which combines the strengths of process-based hydrological models and data-driven approaches to predict spatiotemporal variation in streamflow while quantifying uncertainty. The constrained reasoning and learning (CRL) method integrates physical laws as logical constraints into a deep neural network, allowing for more accurate predictions. To address small data settings, a theoretically-grounded training approach is developed to improve the generalization accuracy of deep models. Additionally, the paper introduces a novel combination of Gaussian processes and deep temporal models for uncertainty quantification. Experimental results on multiple real-world datasets demonstrate the effectiveness of both CRL and GP with deep kernel approaches over strong baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to predict water flow in rivers and streams while also figuring out how certain we are about our predictions. To do this, researchers combined two different approaches: one that uses physical rules to make predictions and another that uses a lot of data. They also came up with new ways to use these methods together to get more accurate results. The team tested their ideas on real-world data and found that they worked better than other methods. |
Keywords
» Artificial intelligence » Generalization » Neural network » Spatiotemporal
