| Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches Y Cai, K Guan, D Lobell, AB Potgieter, S Wang, J Peng, T Xu, S Asseng, ... Agricultural and forest meteorology 274, 144-159, 2019 | 423 | 2019 |
| Machine learning for hydrologic sciences: An introductory overview T Xu, F Liang Wiley Interdisciplinary Reviews: Water, e1533, 2021 | 107 | 2021 |
| A Bayesian approach to improved calibration and prediction of groundwater models with structural error T Xu, AJ Valocchi Water Resources Research 51 (11), 9290-9311, 2015 | 84 | 2015 |
| Data-driven methods to improve baseflow prediction of a regional groundwater model T Xu, AJ Valocchi Computers & Geosciences 85, 124-136, 2015 | 78 | 2015 |
| Quantifying model structural error: Efficient B ayesian calibration of a regional groundwater flow model using surrogates and a data‐driven error model T Xu, AJ Valocchi, M Ye, F Liang Water Resources Research 53 (5), 4084-4105, 2017 | 73 | 2017 |
| Use of machine learning methods to reduce predictive error of groundwater models T Xu, AJ Valocchi, J Choi, E Amir Groundwater 52 (3), 448-460, 2014 | 46 | 2014 |
| Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T Xu, Q Longyang, C Tyson, R Zeng, BT Neilson Water Resources Research 58 (3), 2022 | 30 | 2022 |
| Addressing challenges for mapping irrigated fields in subhumid temperate regions by integrating remote sensing and hydroclimatic data T Xu, JM Deines, AD Kendall, B Basso, DW Hyndman Remote Sensing 11 (3), 370, 2019 | 29 | 2019 |
| Bayesian calibration of groundwater models with input data uncertainty T Xu, AJ Valocchi, M Ye, F Liang, YF Lin Water Resources Research 53 (4), 3224-3245, 2017 | 29 | 2017 |
| Multi-objective optimization of urban environmental system design using machine learning P Li, T Xu, S Wei, ZH Wang Computers, Environment and Urban Systems 94, 101796, 2022 | 20 | 2022 |
| Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China H Hou, Q Longyang, H Su, R Zeng, T Xu, ZH Wang International Journal of Applied Earth Observation and Geoinformation 122 …, 2023 | 18 | 2023 |
| Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains S Wei, T Xu, GY Niu, R Zeng Remote Sensing 14 (13), 3004, 2022 | 15 | 2022 |
| Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling DW Hyndman, T Xu, JM Deines, G Cao, R Nagelkirk, A Viña, ... Geophysical Research Letters 44 (16), 8359-8368, 2017 | 15 | 2017 |
| Learning relational Kalman filtering J Choi, E Amir, T Xu, A Valocchi Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 15 | 2015 |
| A nonparametric sequential data assimilation scheme for soil moisture flow Y Wang, L Shi, T Xu, Q Zhang, M Ye, Y Zha Journal of Hydrology 593, 125865, 2020 | 11 | 2020 |
| Machine learning-based modeling of spatio-temporally varying responses of rainfed corn yield to climate, soil, and management in the US Corn Belt T Xu, K Guan, B Peng, S Wei, L Zhao Frontiers in Artificial Intelligence 4, 647999, 2021 | 10 | 2021 |
| Improving groundwater flow model prediction using complementary data-driven models T Xu, AJ Valocchi, J Choi, E Amir XIX International Conference on Computational Methods in Water Resources …, 2012 | 10 | 2012 |
| Ungaged inflow and loss patterns in urban and agricultural sub‐reaches of the Logan River Observatory H Tennant, BT Neilson, MP Miller, T Xu Hydrological Processes 35 (4), e14097, 2021 | 6 | 2021 |
| Use of data-driven models to improve prediction of physically based groundwater models T Xu University of Illinois at Urbana-Champaign, 2012 | 4 | 2012 |
| Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed C Tyson, Q Longyang, BT Neilson, R Zeng, T Xu Journal of Hydrology 619, 129304, 2023 | 3 | 2023 |
Al ValocchiProfessor of Civil and Environmental Engineering, Univ. of Illinois在 illinois.edu 的电子邮件经过验证
