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
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
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
Data-driven methods to improve baseflow prediction of a regional groundwater model
T Xu, AJ Valocchi
Computers & Geosciences 85, 124-136, 2015
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
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
Learning relational Kalman filtering
J Choi, E Amir, T Xu, AJ Valocchi
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
Machine learning for hydrologic sciences: An introductory overview
T Xu, F Liang
Wiley Interdisciplinary Reviews: Water, e1533, 2021
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
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
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
Use of data-driven models to improve prediction of physically based groundwater models
T Xu
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, 40, 2021
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
A fully Bayesian approach to uncertainty quantification of groundwater models
T Xu
University of Illinois at Urbana-Champaign, 2016
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
Irrigation Water Use Estimation by Integrating In-situ and Remote Sensing Data Using Machine Learning
S Wei, T Xu, R Zeng
AGU Fall Meeting 2021, 2021
Inferring spatiotemporal precipitation-discharge patterns of a snow dominated mountainous karst watershed using a hybrid physically based and deep learning modeling approach
T Xu, Q Longyang, BT Neilson, R Zeng
AGU Fall Meeting 2021, 2021
The system can't perform the operation now. Try again later.
Articles 1–20