引用次数
引用: 1,086
H-指数: 14
I10-指数 : 16
出版物: 46
A Bayesian approach to model structural error and input variability in groundwater modeling
A. J. Valocchi , F. Liang , Y. F. F. Lin , T. Xu
AGU Fall Meeting Abstracts 2015
2015
A Fully Bayesian Approach to Improved Calibration and Prediction of Groundwater Models With Structure Error
A. J. Valocchi , T. Xu
2014 AGU Fall Meeting 2014
2014
A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error
A. J. Valocchi , T. Xu , M. Ye , F. Liang
AGUFM 2016
2016
Effects of Climate Forcing Uncertainty on Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed
T. Xu , B. T. Neilson , Q. Longyang , C. Tyson
AGUFM 2019
2019
Identifying Spatiotemporal Changes In Irrigated Area Across Southwestern Michigan, USA, Using Remote Sensing and Climate Data
D. W. Hyndman , A. D. Kendall , T. Xu , J. M. Deines
AGUFM 2017
2017
Assessing effects of the choice of meteorological forcing datasets and downscaling methods on distributed snow simulations in a mountainous catchment
T. Xu , C. Tyson
AGU Fall Meeting Abstracts 2018
2018
Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
S. Asseng , D. B. Lobell , A. B. Potgieter , J. Peng
AGU Fall Meeting Abstracts 2018
278
2018
Hybrid physically-based and deep learning modeling of a snow dominated mountainous karst watershed
T. Xu , B. T. Neilson , Q. Longyang , C. Tyson
AGU Fall Meeting Abstracts 2019
8
2019
Using Naturally Occurring Tracers to Quantify Components of Urban and Agricultural Streamflow
M. P. Miller , H. Tennant , T. Xu , B. T. Neilson
AGU Fall Meeting Abstracts 2019
2019
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
D. W. Hyndman , A. D. Kendall , T. Xu , J. M. Deines
AGU Fall Meeting Abstracts 2018
2018
Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling
D. W. Hyndman , T. Xu , J. M. Deines , G. Cao
Geophysical Research Letters 44 ( 16) 8359 -8368
9
2017
Learning relational kalman filtering
Albert J. Valocchi , Eyal Amir , Jaesik Choi , Tianfang Xu
national conference on artificial intelligence 2539 -2546
5
2015
Use of Data-driven Models to Enhance Prediction of Groundwater Models
Albert Valocchi , Tianfang Xu
EGU General Assembly Conference Abstracts
2013
Ungaged inflow and loss patterns in urban and agricultural sub‐reaches of the Logan River Observatory
Matthew P. Miller , Hyrum Tennant , Tianfang Xu , Bethany T. Neilson
Hydrological Processes 35 ( 4)
2021
Bayesian calibration of groundwater models with input data uncertainty
Tianfang Xu , Albert J. Valocchi , Ming Ye , Feng Liang
Water Resources Research 53 ( 4) 3224 -3245
10
2017
Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model
Tianfang Xu , Albert J. Valocchi , Ming Ye , Feng Liang
Water Resources Research 53 ( 5) 4084 -4105
62
2017
Data-driven methods to improve baseflow prediction of a regional groundwater model
Tianfang Xu , Albert J. Valocchi
Computers & Geosciences 85 124 -136
62
2015
Use of machine learning methods to reduce predictive error of groundwater models.
Tianfang Xu , Albert J. Valocchi , Jaesik Choi , Eyal Amir
Ground Water 52 ( 3) 448 -460
37
2014
Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data
Tianfang Xu , Jillian M Deines , Anthony D Kendall , Bruno Basso
Remote Sensing 11 ( 3) 370
22
2019
A Bayesian approach to improved calibration and prediction of groundwater models with structural error
Tianfang Xu , Albert J. Valocchi
Water Resources Research 51 ( 11) 9290 -9311
69
2015