Tianfang Xu is an assistant professor in School of Sustainable Engineering and the Built Environment at Arizona State University. She holds a bachelor degree in Geotechnical Engineering from Nanjing University, China, and master’s and doctoral degrees in civil engineering from University of Illinois at Urbana-Champaign. Before joining ASU, she was a postdoctoral researcher at Michigan State University and a research assistant professor in Department of Civil and Environmental Engineering and Utah Water Research Laboratory, Utah State University. Her research focuses on numerical simulation of groundwater flow and solute transport, uncertainty quantification and applications of machine learning in hydrology.
Xu's research includes numerical simulation of groundwater flow and solute transport, uncertainty quantification, and applications of machine learning in hydrology.
Our Groundwater Sustainability and Data Sciences research group combines process-based models with data-driven methods to improve predictive capability and understanding of water resources systems, in particular, under human adaptations and global change. Recent projects include hybrid process-based and deep learning modeling of a snow dominated mountainous karst watershed, using remote sensing and machine learning for crop irrigation monitoring and yield prediction, and HPC-enabled uncertainty quantification for hydrologic models.
Y. Cai, K. Guan, D. Lobell, A. B. Potgieter, S. Wang, J. Peng, T. Xu, S. Asseng, Y. Zhang, L. You, and B. Peng. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and Forest Meteorology, in press.
T. Xu, J. M. Deines, A. Kendall, D. H. Hyndman, and B. Basso. Addressing Challenges for Remotely Sensing Irrigation in Humid Temperate Regions by Incorporating Remote Sensing and Hydroclimatic Data. Remote Sensing, 11(3), 370, 2019.
D. W. Hyndman, T. Xu, J. M. Deines et al. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters, 44(16): 8359-8368, 2017.
T. Xu, A. J. Valocchi, M. Ye and F. Liang. Quantifying model structural error: efficient Bayesian calibration of a regional groundwater flow model with a data-driven error model and fast surrogates. Water Resources Research, 53(5), 4084-4105.
T. Xu, A. J. Valocchi, M. Ye, F. Liang and Y.F. Lin. Bayesian calibration of groundwater models with input data uncertainty. Water Resources Research, doi:10.1002/2016WR01951.
T. Xu and A. J. Valocchi. A Bayesian approach to improved calibration and prediction of groundwater models with structural error. Water Resources Research, 51(11): 9290-9311, 2015.
T. Xu and A. J. Valocchi. Data-driven methods to improve baseflow prediction of a regional groundwater model. Computers & Geosciences, 85(B): 124-136, 2015.Choi, J., E. Amir, T. Xu and A. J. Valocchi. Learning relational Kalman filtering. In Proc. 29th AAAI Conf. on Artificial Intelligence (AAAI-15), Austin, TX, Jan. 2015.
T. Xu, A. J. Valocchi, J. Choi, and E. Amir. Use of machine learning methods to reduce predictive error of groundwater models. Groundwater, 52(3): 448-460, 2014.