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Tiantian Yang, Ph.D.
My name is Tiantian Yang. I am an assistant professor at the University of Oklahoma, School of Civil Engineering and Environmental Science. My research focuses on water resources management, reservoir operation and optimization, coupled natural and human systems, and the interaction among water-energy-climate. Click the bottom Read More to find out more about my education history, work experience, and visions about research and teaching.
Newly Accepted Articles / News
(New) Lujun Zhang and Team's paper accepted in Journal of Hydrology
entitled "Evaluation of Subseasonal-to-Seasonal (S2S) Precipitation Forecast from the North American Multi-Model Ensemble Phase II (NMME-2) over the contiguous U.S."
Tiantian Yang and Team's paper published in Journal of Hydrology
entitled "A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region"
Citation: Yang, T., Zhang, L., Kim, T., Hong, Y., Zhang, D., & Peng, Q. (2021). A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region. Journal of Hydrology, 602, 126723.
Tareem Kim and Team's paper accepted in the Journal of Hydrology
entitled "Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS"
Citation: Kim, T., Yang, T., Gao, S., Zhang, L., Ding, Z., Wen, X., ... & Hong, Y. (2021). Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS. Journal of Hydrology, 598, 126423.
Ziyu Ding and Team's paper accepted in the Applied Energy
entitled "A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system"
Citation: Ding, Z., Wen, X., Tan, Q., Yang, T., Fang, G., Lei, X., ... & Wang, H. (2021). A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system. Applied Energy, 291, 116820.
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