| A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction F Li, Z Gui, Z Zhang, D Peng, S Tian, K Yuan, Y Sun, H Wu, J Gong, Y Lei Neurocomputing 403, 153-166, 2020 | 62 | 2020 |
| A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation K Yuan, X Cheng, Z Gui, F Li, H Wu International Journal of Geographical Information Science 33 (12), 2455-2476, 2019 | 28 | 2019 |
| Deforestation reshapes land-surface energy-flux partitioning K Yuan, Q Zhu, S Zheng, L Zhao, M Chen, WJ Riley, X Cai, H Ma, F Li, ... Environmental Research Letters 16 (2), 024014, 2021 | 18 | 2021 |
| Understanding and reducing the uncertainties of land surface energy flux partitioning within CMIP6 land models K Yuan, Q Zhu, WJ Riley, F Li, H Wu Agricultural and Forest Meteorology 319, 108920, 2022 | 13 | 2022 |
| Causality guided machine learning model on wetland CH4 emissions across global wetlands K Yuan, Q Zhu, F Li, WJ Riley, M Torn, H Chu, G McNicol, M Chen, S Knox, ... Agricultural and Forest Meteorology 324, 109115, 2022 | 10 | 2022 |
| Building a machine learning surrogate model for wildfire activities within a global Earth system model Q Zhu, F Li, WJ Riley, L Xu, L Zhao, K Yuan, H Wu, J Gong, J Randerson Geoscientific Model Development 15 (5), 1899-1911, 2022 | 9 | 2022 |
| Spatiotemporal analysis and prediction of crime events in atlanta using deep learning S Wang, K Yuan 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC …, 2019 | 9 | 2019 |
| Wetter California projected by CMIP6 models with observational constraints under a high GHG emission scenario F Li, Q Zhu, WJ Riley, K Yuan, H Wu, Z Gui Earth's Future 10 (4), e2022EF002694, 2022 | 6 | 2022 |
| Vegetation clumping modulates global photosynthesis through adjusting canopy light environment F Li, D Hao, Q Zhu, K Yuan, RK Braghiere, L He, X Luo, S Wei, WJ Riley, ... Global Change Biology 29 (3), 731-746, 2023 | 4 | 2023 |
| AttentionFire_v1. 0: interpretable machine learning fire model for burned-area predictions over tropics F Li, Q Zhu, WJ Riley, L Zhao, L Xu, K Yuan, M Chen, H Wu, Z Gui, J Gong, ... Geoscientific Model Development 16 (3), 869-884, 2023 | 3 | 2023 |
| Building a machine learning surrogate model for wildfire activities within a global earth system model Q Zhu, F Li, WJ Riley, L Xu, L Zhao, K Yuan, H Wu, J Gong, JT Randerson Geoscientific Model Development Discussions 2021, 1-22, 2021 | 2 | 2021 |
| Predicting climate conditions based on teleconnections H Ma, K Yuan, F Li, C Leroy, G Bronevetsky US Patent 11,243,332, 2022 | 1 | 2022 |
| Predicting climate conditions based on teleconnections H Ma, K Yuan, F Li, C Leroy, G Bronevetsky US Patent 11,668,856, 2023 | | 2023 |
| Impacts of foliage clumping on global photosynthesis F Li, D Hao, Q Zhu, K Yuan, L He, RK Braghiere, X Luo, S Wei, WJ Riley, ... AGU Fall Meeting Abstracts 2022, B45B-04, 2022 | | 2022 |
| Building FLUXNET-CH4 2.0: Methane Flux Data and Community to Advance Global Methane Cycle Science G McNicol, S Knox, KB Delwiche, A Hoyt, O Briner, K Yuan, Q Zhu, ... AGU Fall Meeting Abstracts 2022, INV22C-0508, 2022 | | 2022 |
| Informing the Inclusive Growth of FLUXNET-CH4 Data and Community via Representativeness Analyses O Briner, K Yuan, Q Zhu, M Chen, G McNicol AGU Fall Meeting Abstracts 2022, INV22C-0510, 2022 | | 2022 |
| Upscaling Wetland Methane Emissions with a Causality Guided Machine Learning Model K Yuan, Q Zhu, M Chen, G McNicol, WJ Riley, S Knox, A Hoyt AGU Fall Meeting Abstracts 2022, INV24A-05, 2022 | | 2022 |
| AttentionFire_v1. 0: interpretable machine learning fire model for burned area predictions over tropics F Li, Q Zhu, W Riley, L Zhao, L Xu, K Yuan, M Chen, H Wu, Z Gui, J Gong, ... Geoscientific Model Development Discussions 2022, 1-28, 2022 | | 2022 |
| Towards robust modeling and upscaling of wetland CH4 emission using FLUXNET-CH4 dataset, remote sensing and machine learning Q Zhu, K Yuan, G McNicol, M Chen, S Knox, A Hoyt, W Riley AGU Fall Meeting Abstracts 2021, B25G-1551, 2021 | | 2021 |
| Upscaling global wetland methane emissions with causality guided machine learning Authors/Affiliations K Yuan, Q Zhu, W Riley, G McNicol, F Li, M Chen | | |
FA LIUniversity of Wisconsin-Madison在 wisc.edu 的电子邮件经过验证
