Tianfang Xu
Cited by
Cited by
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
Machine learning for hydrologic sciences: An introductory overview
T Xu, F Liang
Wiley Interdisciplinary Reviews: Water, e1533, 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
Data-driven methods to improve baseflow prediction of a regional groundwater model
T Xu, AJ Valocchi
Computers & Geosciences 85, 124-136, 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
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
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
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
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
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
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
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
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 Viņa, ...
Geophysical Research Letters 44 (16), 8359-8368, 2017
Learning relational Kalman filtering
J Choi, E Amir, T Xu, A Valocchi
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 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
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
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
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
Use of data-driven models to improve prediction of physically based groundwater models
T Xu
University of Illinois at Urbana-Champaign, 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
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