Tianfang Xu
Assistant Professor, School of Sustainable Engineering and the Built Environment, Ira A. Fulton Schools of Engineering
School of Sustainable Engineering and the Built Environment
Arizona State University
PO Box 873005
Tempe, AZ 85287-3005
Titles
- Senior Global Futures Scientist, Julie Ann Wrigley Global Futures Laboratory
- Assistant Professor, School of Sustainable Engineering and the Built Environment, Ira A. Fulton Schools of Engineering
Biography
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 interests include numerical simulation of groundwater flow and solute transport, uncertainty quantification, and applications of machine learning in hydrology.
Education
- PhD, Civil Engineering, University of Illinois, 2016
- MS, Civil Engineering, University of Illinois, 2012
- BS, Geotechnical Engineering, Nanjing University, 2010
Expertise
Journal Articles
2019
Cai, Y., K. Guan, D. B. Lobell, A. B. Potgieter, S. Wang, J. Peng, T. Xu, S. Asseng, Y. Zhang, L. You and B. Peng. 2019. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and Forest Meteorology 274(Aug):144-159. DOI: 10.1016/j.agrformet.2019.03.010. (link )
Xu, T., J. M. Deines, A. D. Kendall, B. Basso and D. W. Hyndman. 2019. Addressing challenges for mapped irrigated fields in subhumid temperature regions by integrating remote sensing and hydroclimate data. Remote Sensing 11(3):370. DOI: 10.3390/rs11030370. (link )
2017
Hyndman, D. W., T. Xu, J. M. Deines, G. Cao, R. Nagelkirk, A. Vina, W. McConnell, B. Basso, A. D. Kendall, S. Li, F. Lupi, D. Ma, J. A. Winkler, W. Yang, C. Zhang and J. Liu. 2017. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters 44(16):8359–8368. DOI: 10.1002/2017GL074429. (link )
Xu, T., A. J. Valocchi, M. Ye and F. Liang. 2017. Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data‐driven error model. Water Resources Research 53(5):4084–4105. DOI: 10.1002/2016WR019831. (link )
Xu, T., A. J. Valocchi, M. Ye, F. Liang and Y. Lin. 2017. Bayesian calibration of groundwater models with input data uncertainty. Water Resources Research 53(4):3224–3245. DOI: 10.1002/2016WR019512. (link )
2015
Xu, T. and A. J. Valocchi. 2015. A Bayesian approach to improved calibration and prediction of groundwater models with structural error. Water Resources Research 51(11):9290– 9311. DOI: 10.1002/2015WR017912. (link )
Xu, T. and A. J. Valocchi. 2015. Data-driven methods to improve baseflow prediction of a regional groundwater model. Computers & Geosciences 85, Part B(Dec):124-136. DOI: 10.1016/j.cageo.2015.05.016. (link )
2014
Xu, T., A. J. Valocchi, J. Choi and E. Amir. 2014. Use of machine learning methods to reduce predictive error of groundwater models. Groundwater 52(3):448-460. DOI: 10.1111/gwat.12061. (link )
Conference Papers
2015
Choi, J., E. Amir, T. Xu and A. J. Valocchi. 2015. Learning relational Kalman filtering. Pp. 2539-2546 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Twenty-Ninth AAAI Conference on Artificial Intelligence. (link )