Tianfang Xu

Tyson C, Longyang Q, Neilson BT, Zeng R, & Xu T. (2023). Effects of Meteorological Forcing Uncertainty on High-Resolution Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed. Journal of Hydrology, 129304.

Rudko N, Muenich RL, Garcia M, & Xu T. (2023). Development of a point-source model to improve simulations of manure lagoon interactions with the environment. Journal of Environmental Management, 325:116332.

Li, P., Xu, T., Wei, S., & Wang, Z. H. (2022). Multi-objective optimization of urban environmental system design using machine learning. Computers, Environment and Urban Systems, 94, 101796.

Xu, T., Longyang, Q., Tyson, C., Zeng, R., & Neilson, B. T. (2022). Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed. Water Resources Research, 58(3), e2021WR030993.

Xu, T., Guan, K., Peng, B., Wei, S., & Zhao, L. (2021). Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the US Corn Belt. Frontiers in artificial intelligence, 4, 40.

Xu, T., & Liang, F. (2021). Machine learning for hydrologic sciences: An introductory overview. Wiley Interdisciplinary Reviews: Water, 8(5), e1533.

Tennant, H., Neilson, B.T., Miller, M.P. and Xu, T. (2021). Ungaged inflow and loss patterns in urban and agricultural sub‐reaches of the Logan River Observatory. Hydrological Processes, doi: 10.1002/hyp.14097.

Wang, Y., Shi, L., Xu, T., Zhang, Q., Ye, M. and Zha, Y. (2021). A nonparametric sequential data assimilation scheme for soil moisture flow. Journal of Hydrology, 593, doi: 10.1016/j.jhydrol.2020.125865.

Cai, Y., Guan, K., Lobell, D., Potgieter, A.B., Wang, S., Peng, J., Xu, T., Asseng, S., Zhang, Y., You, L. and Peng, B. (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and forest meteorology, 274, pp.144-159.

Xu, T., Deines, J. M., Kendall, A. D., Basso, B., & Hyndman, D. W. (2019). Addressing challenges for mapping irrigated fields in subhumid temperate regions by integrating remote sensing and hydroclimatic data. Remote Sensing, 11(3), 370.

Hyndman, D.W., Xu, T., Deines, J.M., Cao, G., Nagelkirk, R., Viña, A., McConnell, W., Basso, B., Kendall, A.D., Li, S. and Luo, L., 2017. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters, 44(16), pp.8359-8368.

Xu, T., Valocchi, A. J., Ye, M., & Liang, F. (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.

Xu, T., Valocchi, A. J., Ye, M., Liang, F., & Lin, Y. F. (2017). Bayesian calibration of groundwater models with input data uncertainty. Water Resources Research, 53(4), 3224-3245.

Xu, T., & Valocchi, A. J. (2015). A Bayesian approach to improved calibration and prediction of groundwater models with structural error. Water Resources Research, 51(11), 9290-9311.

Xu, T. and Valocchi, A.J., 2015. Data-driven methods to improve baseflow prediction of a regional groundwater model. Computers & Geosciences, 85, pp.124-136.

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.

Xu, T., Valocchi, A. J., Choi, J., & Amir, E. (2014). Use of machine learning methods to reduce predictive error of groundwater models. Groundwater, 52(3), 448-460.​