High-Fidelity Deep Approximation of Ecosystem Simulation over Long-Term at Large Scale

Author:

Wang Zhihao1ORCID,Xie Yiqun1ORCID,Jia Xiaowei2ORCID,Ma Lei1ORCID,Hurtt George1ORCID

Affiliation:

1. University of Maryland, College Park, United States

2. University of Pittsburgh, Pittsburgh, United States

Funder

NSF (National Science Foundation)

National Aeronautics and Space Administration

USGS

Google

University of Maryland

University of Pittsburgh

Publisher

ACM

Reference43 articles.

1. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity

2. Kavosh Asadi , Dipendra Misra , Seungchan Kim , and Michel L Littman . 2019. Combating the compounding-error problem with a multi-step model. arXiv preprint arXiv:1905.13320 ( 2019 ). Kavosh Asadi, Dipendra Misra, Seungchan Kim, and Michel L Littman. 2019. Combating the compounding-error problem with a multi-step model. arXiv preprint arXiv:1905.13320 (2019).

3. Jordan T Ash , Chicheng Zhang , Akshay Krishnamurthy , John Langford , and Alekh Agarwal . 2019. Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv preprint arXiv:1906.03671 ( 2019 ). Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2019. Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv preprint arXiv:1906.03671 (2019).

4. Elena M Bennett , Garry D Peterson , and Line J Gordon . 2009. Understanding relationships among multiple ecosystem services. Ecology letters 12, 12 ( 2009 ), 1394--1404. Elena M Bennett, Garry D Peterson, and Line J Gordon. 2009. Understanding relationships among multiple ecosystem services. Ecology letters 12, 12 (2009), 1394--1404.

5. Leo Breiman . 2001. Random forests. Machine learning 45 ( 2001 ), 5--32. Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5--32.

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