A guide to model hydrological system using the real-world CAMELS dataset, which contains weather drivers for 531 basins across the continental United States. Through this modeling process, we will demonstrate various methods to predict streamflow, aiding in flood and drought planning.
Authors:
- Kshitij Tayal, Oak Ridge National Labs, tayalk@ornl.gov
- Arvind Renganathan, University of Minnesota, renga016@umn.edu
- Siyan Liu, Oak Ridge National Labs, lius1@ornl.gov
- Dan Lu, Oak Ridge National Labs, lud1@ornl.gov
Originally presented at ICLR 2024
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Estimated time to execute end-to-end: 20 minutes
Data used in this tutorial is available at https://doi.org/10.5281/zenodo.14612905
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Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Tayal, K., Renganathan, A., Liu, S., & Lu, D. (2024). Planning for Floods & Droughts: Intro to AI-Driven Hydrological Modeling [Tutorial]. In International Conference on Learning Representations. Climate Change AI. https://doi.org/10.5281/zenodo.14612905
@misc{tayal2024planning,
title={Planning for Floods & Droughts: Intro to AI-Driven Hydrological Modeling},
author={Tayal, Kshitij and Renganathan, Arvind and Liu, Siyan and Lu, Dan},
year={2024},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.14612905},
booktitle={International Conference on Learning Representations},
howpublished={\url{https://github.com/climatechange-ai-tutorials/camels-hydrological-modeling}}
}