Tianfang Xu
Title
Cited by
Cited by
Year
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
382019
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
342015
Quantifying model structural error: Efficient Bayesian 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
322017
Data-driven methods to improve baseflow prediction of a regional groundwater model
T Xu, AJ Valocchi
Computers & Geosciences 85, 124-136, 2015
242015
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
192014
Learning Relational Kalman Filtering.
J Choi, E Amir, T Xu, AJ Valocchi
AAAI, 2539-2546, 2015
112015
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
102017
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
82017
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
72012
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
62019
Use of data-driven models to improve prediction of physically based groundwater models
T Xu
32012
Hybrid physically-based and deep learning modeling of a snow dominated mountainous karst watershed
T Xu, Q Longyang, C Tyson, R Zeng, BT Neilson, DG Tarboton
AGUFM 2019, H32D-02, 2019
12019
A fully Bayesian approach to uncertainty quantification of groundwater models
T Xu
University of Illinois at Urbana-Champaign, 2016
12016
Effects of Climate Forcing Uncertainty on Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed
Q Longyang, C Tyson, T Xu, R Zeng, BT Neilson
AGU Fall Meeting 2019, 2019
2019
Effects of Climate Forcing Uncertainty on Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed
C Tyson, Q Longyang, T Xu, R Zeng, BT Neilson
AGUFM 2019, H33I-2033, 2019
2019
Using Naturally Occurring Tracers to Quantify Components of Urban and Agricultural Streamflow
H Tennant, BT Neilson, MP Miller, T Xu, PD Brooks
AGUFM 2019, H23D-02, 2019
2019
Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
Y Cai, K Guan, DB Lobell, AB Potgieter, SW Wang, J Peng, T Xu, ...
AGUFM 2018, GC43B-01, 2018
2018
Assessing effects of the choice of meteorological forcing datasets and downscaling methods on distributed snow simulations in a mountainous catchment
T Xu, C Tyson
AGUFM 2018, C13H-1225, 2018
2018
Improving classification of humid-region irrigation using the red-edge band of Sentinel-2: Comparing irrigated and non-irrigated corn and soy in southwestern Michigan
P Drippe, AD Kendall, T Xu, JM Deines, DW Hyndman
AGUFM 2018, GC51G-0872, 2018
2018
Identifying Spatiotemporal Changes In Irrigated Area Across Southwestern Michigan, USA, Using Remote Sensing and Climate Data
T Xu, JM Deines, AD Kendall, DW Hyndman
AGUFM 2017, H13I-1523, 2017
2017
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Articles 1–20