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Displaying 1 - 10 of 159 Publications- … reduce Greenhouse gas (GHG) emission, the application of machine-learning (ML) models can help reduce the requirement of … R.; Xin, L.; Blanc-Betes, E.; Zhang, Z.; Wang, J. 2025. A machine learning model using the snapshot ensemble approach …AuthorsS.N. Ferdous, J.P. Ahire, R. Bergman, L. Xin, E. Blanc-Betes, Z. Zhang, J. WangKeywordsSourceJournal: Ecological InformaticsYear2025
- … modern satellite records from 2001 to 2020. Using ensemble machine learning and Earth system models, we found this exceptional …AuthorsYulong Zhang, Jiafu Mao, Ge Sun, Qinfeng Guo, Jeffrey Atkins, Wenhong Li, Mingzhou Jin, Conghe Song, Jingfeng Xiao, Taehee Hwang, Tong Qiu, Lin Meng, Daniel M. Ricciuto, Xiaoying Shi, Xing Li, Peter Thornton, Forrest HoffmanKeywordsSourceRemote Sensing of EnvironmentYear2025
- … hindering targeted prevention efforts. We developed a machine learning model of wildfire ignition cause across the western …AuthorsYavar Pourmohamad, John T. Abatzoglou, Erica Fleishman, Karen C. Short, Jacquelyn Shuman, Amir AghaKouchak, Matthew Williamson, Seyd Teymoor Seydi, Mojtaba SadeghKeywordsSourceEarth's Future, 13: e2024EF005187.Year2025
- … finding 257,266 relevant studies on NCS co-impacts. Using machine learning methods to extract data (for example, study …AuthorsCharlotte H. Chang, James T. Erbaugh, Paola Fajardo, Luci Lu, István Molnár, Dávid Papp, Brian E. Robinson, Kemen G. Austin, Miguel Castro, Samantha H. Cheng, Susan Cook-Patton, Peter W. Ellis, Teevrat Garg, Jacob P. Hochard, Timm Kroeger, Robert I McDonald, Erin E. Poor, Lindsey S. Smart, Andrew R. Tilman, Preston Welker, Stephen A. Wood, Yuta J. MasudaSourceNature SustainabilityYear2024
- … … Remote sensing … machine learning … forest mensuration … forest inventory. … …AuthorsDiogo Cosenza, Svetlana Saarela, Jacob Strunk, Lauri Korhonen, Matti Maltamo, Petteri PackalenSourceForestry: An International Journal of Forest Research. 32: cpae055.Year2024
- … C-band SAR, and topography data were used as inputs in a machine learning classifier to identify was used to map 12 land cover …AuthorsMichael J. Battaglia, Angela Lafuente, Juan C. Benavides, Erik A. Lilleskov, Rodney A. Chimner, Laura L. Bourgeau-Chavez, Patrick Nicolás. Skillings-NeiraKeywordsSourceFrontiers in ClimateYear2024
- … of aerial firefighting operations. Using sequential machine learning models, specifically Long Short-Term Memory (LSTM) …AuthorsShayne Magstadt, Yu Wei, Bradley M. Pietruszka, David E. CalkinKeywordsSourceFire. 7: 380.Year2024
- … indices … systematic conservation planning … forestry … machine learning. … Philosophical Transactions of the Royal Society …AuthorsYuanheng Li, Christian Devenish, Marie I Tosa, Mingjie Luo, David M. Bell, Damon B. Lesmeister, Paul Greenfield, Maximilian Pichler, Taal Levi, Douglas W. YuKeywordsSourcePhilosophical Transactions of the Royal Society B: Biological Sciences. 379(1904): e37986.Year2024
- … in the western United States that are in high demand. Machine learning is a promising field with the ability to model …AuthorsKevin Young, Erin Belval, Karin Riley, Peng GaoKeywordsSourceJournal of Environmental Management. 370: 122705.Year2024
- … for predicting habitat selection of animals. Recently, machine-learning methods such as random forest have gained popularity … Winston; Sager‑Fradkin, Kimberly; Robinson, Hugh. 2024. Machine learning allows for large‑scale habitat prediction of …AuthorsW. Connor O’Malley, L. Mark Elbroch, Katherine A. Zeller, Paul Beier, Meghan M. Beale, Richard A. Beausoleil, Brian Kertson, Kyle Knopff, Kryan Kunkel, Benjamin T. Maletzke, Quinton Martins, Marc R. Matchett, Christopher C. Wilmers, Heiko U. Wittmer, Winston Vickers, Kimberly Sager‑Fradkin, Hugh RobinsonKeywordsSourceLandscape Ecology. 39: 106.Year2024