Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2839-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2839-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Apr 2022
Development and technical paper |
| 07 Apr 2022
| 07 Apr 2022
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
Licheng Liu
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Shaoming Xu
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Jinyun Tang
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Kaiyu Guan
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Timothy J. Griffis
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Matthew D. Erickson
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Alexander L. Frie
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Xiaowei Jia
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA
Taegon Kim
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Department of Smart Farm, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
Lee T. Miller
Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN 55108, USA
Bin Peng
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Shaowei Wu
School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA
Yufeng Yang
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Wang Zhou
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Vipin Kumar
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Zhenong Jin
CORRESPONDING AUTHOR
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
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Cited
20 citations as recorded by crossref.
- Advancing agroecosystem modelling of nitrogen losses with machine learning S. Lam et al. 10.1016/j.ecz.2024.100006
- Enhancing spectroscopy-based fruit quality control: A knowledge-guided machine learning approach to reduce model uncertainty J. Yang et al. 10.1016/j.postharvbio.2024.113009
- A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest Q. Yang et al. 10.1016/j.rse.2023.113880
- Applying Knowledge-Guided Machine Learning to Slope Stability Prediction T. Pei et al. 10.1061/JGGEFK.GTENG-11053
- Toward impact-based monitoring of drought and its cascading hazards A. AghaKouchak et al. 10.1038/s43017-023-00457-2
- Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision Q. Yang et al. 10.1016/j.compag.2024.108911
- A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. 10.1016/j.earscirev.2023.104462
- Transfer learning in environmental remote sensing Y. Ma et al. 10.1016/j.rse.2023.113924
- Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models L. Li et al. 10.1007/s00376-023-3181-8
- Developing a hybrid data-driven and informed model for prediction and mitigation of agricultural nitrous oxide flux hotspots N. Vemuri 10.3389/fenvs.2024.1353049
- Effects of agricultural management and of climate change on N2O emissions in an area of the Brazilian Cerrado: Measurements and simulations using the STICS soil-crop model F. da Silva et al. 10.1016/j.agee.2023.108842
- Cyberinfrastructure for sustainability sciences C. Song et al. 10.1088/1748-9326/acd9dd
- Nitrogen fertilizers and the future of sustainable agriculture: a deep dive into production, pollution, and mitigation measures M. Tufail et al. 10.1080/00380768.2024.2361068
- Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast P. Xue et al. 10.3390/rs14112640
- A deep transfer learning framework for mapping high spatiotemporal resolution LAI J. Zhou et al. 10.1016/j.isprsjprs.2023.10.017
- Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L. Liu et al. 10.1038/s41467-023-43860-5
- The Cycles agroecosystem model: Fundamentals, testing, and applications A. Kemanian et al. 10.1016/j.compag.2024.109510
- Reconstructing Turbulent Flows Using Spatio-temporal Physical Dynamics S. Chen et al. 10.1145/3637491
- Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States H. Wander et al. 10.1021/acs.est.3c05784
- KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments L. Liu et al. 10.5194/gmd-15-2839-2022
19 citations as recorded by crossref.
- Advancing agroecosystem modelling of nitrogen losses with machine learning S. Lam et al. 10.1016/j.ecz.2024.100006
- Enhancing spectroscopy-based fruit quality control: A knowledge-guided machine learning approach to reduce model uncertainty J. Yang et al. 10.1016/j.postharvbio.2024.113009
- A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest Q. Yang et al. 10.1016/j.rse.2023.113880
- Applying Knowledge-Guided Machine Learning to Slope Stability Prediction T. Pei et al. 10.1061/JGGEFK.GTENG-11053
- Toward impact-based monitoring of drought and its cascading hazards A. AghaKouchak et al. 10.1038/s43017-023-00457-2
- Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision Q. Yang et al. 10.1016/j.compag.2024.108911
- A scalable framework for quantifying field-level agricultural carbon outcomes K. Guan et al. 10.1016/j.earscirev.2023.104462
- Transfer learning in environmental remote sensing Y. Ma et al. 10.1016/j.rse.2023.113924
- Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models L. Li et al. 10.1007/s00376-023-3181-8
- Developing a hybrid data-driven and informed model for prediction and mitigation of agricultural nitrous oxide flux hotspots N. Vemuri 10.3389/fenvs.2024.1353049
- Effects of agricultural management and of climate change on N2O emissions in an area of the Brazilian Cerrado: Measurements and simulations using the STICS soil-crop model F. da Silva et al. 10.1016/j.agee.2023.108842
- Cyberinfrastructure for sustainability sciences C. Song et al. 10.1088/1748-9326/acd9dd
- Nitrogen fertilizers and the future of sustainable agriculture: a deep dive into production, pollution, and mitigation measures M. Tufail et al. 10.1080/00380768.2024.2361068
- Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast P. Xue et al. 10.3390/rs14112640
- A deep transfer learning framework for mapping high spatiotemporal resolution LAI J. Zhou et al. 10.1016/j.isprsjprs.2023.10.017
- Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L. Liu et al. 10.1038/s41467-023-43860-5
- The Cycles agroecosystem model: Fundamentals, testing, and applications A. Kemanian et al. 10.1016/j.compag.2024.109510
- Reconstructing Turbulent Flows Using Spatio-temporal Physical Dynamics S. Chen et al. 10.1145/3637491
- Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States H. Wander et al. 10.1021/acs.est.3c05784
Latest update: 23 Nov 2024
Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the...
