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Modeling environmental ecosystems is critical for the sustainability of our planet, but is extremely challenging due to the complex underlying processes driven by interactions amongst a large number of physical variables.
Debates—the future of hydrological sciences: A (common) path forward? using models and data to learn: A systems theoretic perspective on the future of hydrological science
Hoshin V Gupta and Grey S Nearing · 2014
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Summary of hydrologic modeling for the delaware river basin using the water availability tool for environmental resources (water)
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Attention is all you need
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Bringing automated, remote-sensed, machine learning methods to monitoring crop landscapes at scale
Xiaowei Jia, Ankush Khandelwal, David J Mulla, Philip G Pardey, and Vipin Kumar · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the lorenz 96 model
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Zach Moshe, Asher Metzger, Gal Elidan, Frederik Kratzert, Sella Nevo, and Ran El-Yaniv · 2020
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Heterogeneous stream-reservoir graph networks with data assimilation
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Physics-guided recurrent graph model for predicting flow and temperature in river networks
Xiaowei Jia, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael Steinbach, Jordan Read, et al · 2021
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Applications of deep learning in hydrology
Chaopeng Shen and Kathryn Lawson · 2021
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Explore spatio-temporal learning of large sample hydrology using graph neural networks
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Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for us midwestern agroecosystems
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Tackling climate change with machine learning
David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, et al · 2022
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Physics-guided meta-learning method in baseflow prediction over large regions
Shengyu Chen, Yiqun Xie, Xiang Li, Xu Liang, and Xiaowei Jia · 2023
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Physics guided neural networks for time-aware fairness: an application in crop yield prediction
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On the opportunities and challenges of foundation models for geospatial artificial intelligence
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Anypredict: Foundation model for tabular prediction
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A gnn-rnn approach for harnessing geospatial and temporal information: application to crop yield prediction
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Large language models are effective table-to-text generators, evaluators, and feedback providers
Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, and Arman Cohan · 2023
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Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions
Jacob A Zwart, Samantha K Oliver, William David Watkins, Jeffrey M Sadler, Alison P Appling, Hayley R Corson-Dosch, Xiaowei Jia, Vipin Kumar, and Jordan S Read · 2023
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