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The modeling of environmental ecosystems plays a pivotal role in the sustainable management of our planet.
Instream water temperature model. instream flow information paper 16
Fred D Theurer, Kenneth A Voos, and William J Miller · 1984
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A hybrid neural network and arima model for water quality time series prediction
Durdu Ömer Faruk · 2010
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Changes in net ecosystem productivity and greenhouse gas exchange with fertilization of douglas fir: Mathematical modeling in ecosys
RF Grant, T Andrew Black, Rachhpal S Jassal, and Christian Bruemmer · 2010
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Swat ungauged: hydrological budget and crop yield predictions in the upper mississippi river basin
Raghavan Srinivasan, Xuesong Zhang, and Jeffrey Arnold · 2010
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Multi-prediction deep boltzmann machines
Ian Goodfellow, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
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Prms-iv, the precipitation-runoff modeling system, version 4
Steven L Markstrom, R Steve Regan, Lauren E Hay, Roland J Viger, Richard M Webb, Robert A Payn, and Jacob H LaFontaine · 2015
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National water information system data available on the world wide web (usgs water data for the nation), 2016
US Geological Survey · 2016
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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Water quality data for national-scale aquatic research: The water quality portal
Emily K Read, Lindsay Carr, Laura De Cicco, Hilary A Dugan, Paul C Hanson, Julia A Hart, James Kreft, Jordan S Read, and Luke A Winslow · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Mida: Multiple imputation using denoising autoencoders
Lovedeep Gondara and Ke Wang · 2018
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Description of the national hydrologic model for use with the precipitation-runoff modeling system (prms)
R Steven Regan, Steven L Markstrom, Lauren E Hay, Roland J Viger, Parker A Norton, Jessica M Driscoll, and Jacob H LaFontaine · 2018
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Processing of missing data by neural networks
Marek Śmieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, and Przemysław Spurek · 2018
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Deep multi-view spatial-temporal network for taxi demand prediction
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li · 2018
Cited alongside, same era.
Gain: Missing data imputation using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Schaar · 2018
Cited alongside, same era.
Advanced terrestrial simulator
Ethan Coon, Daniil Svyatsky, Ahmad Jan, Eugene Kikinzon, Markus Berndt, Adam Atchley, Dylan Harp, Gianmarco Manzini, Eitan Shelef, Konstantin Lipnikov, Rao Garimella, Chonggang Xu, David Moulton, Satish Karra, Scott Painter, Elchin Jafarov, and Sergi Molins · 2019
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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
Cited alongside, same era.
Process-guided deep learning predictions of lake water temperature
Jordan S Read, Xiaowei Jia, Jared Willard, Alison P Appling, Jacob A Zwart, Samantha K Oliver, Anuj Karpatne, Gretchen JA Hansen, Paul C Hanson, William Watkins, et al · 2019
Cited alongside, same era.
Explore spatio-temporal learning of large sample hydrology using graph neural networks
Alexander Y Sun, Peishi Jiang, Maruti K Mudunuru, and Xingyuan Chen · 2021
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Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for us midwestern agroecosystems
Wang Zhou, Kaiyu Guan, Bin Peng, Jinyun Tang, Zhenong Jin, Chongya Jiang, Robert Grant, and Symon Mezbahuddin · 2021
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Kgml-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating n 2 o emission using data from mesocosm experiments
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J Griffis, Matthew D Erickson, Alexander L Frie, Xiaowei Jia, Taegon Kim, Lee T Miller, et al · 2022
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Are multimodal transformers robust to missing modality?
Mengmeng Ma, Jian Ren, Long Zhao, Davide Testuggine, and Xi Peng · 2022
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Graph-guided network for irregularly sampled multivariate time series
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Cited alongside, same era.
Hydronets: Leveraging river structure for hydrologic modeling
Zach Moshe, Asher Metzger, Gal Elidan, Frederik Kratzert, Sella Nevo, and Ran El-Yaniv · 2020
Cited alongside, same era.
Missing data imputation with adversarially-trained graph convolutional networks
Indro Spinelli, Simone Scardapane, and Aurelio Uncini · 2020
Cited alongside, same era.
Convolutional tensor-train lstm for spatio-temporal learning
Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, and Anima Anandkumar · 2020
Cited alongside, same era.
Agricultural diversification promotes multiple ecosystem services without compromising yield
Giovanni Tamburini, Riccardo Bommarco, Thomas Cherico Wanger, Claire Kremen, Marcel GA Van Der Heijden, Matt Liebman, and Sara Hallin · 2020
Cited alongside, same era.
Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang · 2020
Cited alongside, same era.
Heterogeneous stream-reservoir graph networks with data assimilation
Shengyu Chen, Alison Appling, Samantha Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Sadler, Jacob Zwart, and Xiaowei Jia · 2021
Cited alongside, same era.
Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik · 2022
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Climate change and the urgency to transform food systems
Monika Zurek, Aniek Hebinck, and Odirilwe Selomane · 2022
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Scalable spatiotemporal graph neural networks
Andrea Cini, Ivan Marisca, Filippo Maria Bianchi, and Cesare Alippi · 2023
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Global water use efficiency saturation due to increased vapor pressure deficit
Fei Li, Jingfeng Xiao, Jiquan Chen, Ashley Ballantyne, Ke Jin, Bing Li, Michael Abraha, and Ranjeet John · 2023
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Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting
Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, and Xuan Song · 2023
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Free: The foundational semantic recognition for modeling environmental ecosystems
Shiyuan Luo, Juntong Ni, Shengyu Chen, Runlong Yu, Yiqun Xie, Licheng Liu, Zhenong Jin, Huaxiu Yao, and Xiaowei Jia · 2023
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Cultural water and indigenous water science
Erin O’Donnell, Melissa Kennedy, Dustin Garrick, Avril Horne, and Rene Woods · 2023
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Swinlstm: Improving spatiotemporal prediction accuracy using swin transformer and lstm
Song Tang, Chuang Li, Pu Zhang, and RongNian Tang · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Rapid groundwater decline and some cases of recovery in aquifers globally
Scott Jasechko, Hansjörg Seybold, Debra Perrone, Ying Fan, Mohammad Shamsudduha, Richard G Taylor, Othman Fallatah, and James W Kirchner · 2024
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Time series as images: Vision transformer for irregularly sampled time series
Zekun Li, Shiyang Li, and Xifeng Yan · 2024
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