研究队伍
于江
背景介绍
于江，宁波市东方理工高等研究院博士后，北京大学博士。主要研究方向为流域尺度水文与营养盐过程与陆海相互作用，研究内容包括流域水文和水质模拟，陆海水文和营养盐模型耦合，海底地下水排泄，水文数据分析和机器学习等。
研究领域
流域尺度水文与营养盐过程模拟
近岸海域海底地下水排泄研究
陆海相互作用与陆海模型耦合
机器学习和大数据在水文领域的应用
教育背景
2018-2023：北京大学，力学（能源与资源工程），博士
2014-2018：北京大学，力学（能源与动力工程），学士
研究成果
1. Yu, J., Tian, Y., Wang, X., Wang, X., Lancia, M., Andrews, C. B., & Chen, K. (2022). A new Simulation‐Optimization framework for estimation of submarine groundwater discharge based on hydrodynamic modeling and isotopic data. Geophysical Research Letters, 49(23). https://doi.org/10.1029/2022gl098893
2. Yu, J., Tian, Y., Jing, H., Sun, T., Wang, X., Andrews, C. B., & Chen, K. (2023). Predicting regional wastewater treatment plant discharges using machine learning and population migration big data. ACS ES&T Water, 3(5), 1314–1328. https://doi.org/10.1021/acsestwater.2c00639
3. Yu, J., Tian, Y., Wang, X., & Chen, K. (2021). Using machine learning to reveal spatiotemporal complexity and driving forces of water quality changes in Hong Kong marine water. Journal of Hydrology, 603, 126841. https://doi.org/10.1016/j.jhydrol.2021.126841
4. Wang, X., Tian, Y., Yu, J., Lancia, M., Chen, J., Xiao, K., Zheng, Y., Andrews, C. B., & Zheng, C. (2023). Complex effects of tides on coastal groundwater revealed by High‐Resolution Integrated Flow Modeling. Water Resources Research, 59(10). https://doi.org/10.1029/2022wr033942
5. Wei, X., Yu, J., Tian, Y., Ben, Y., Cai, Z., & Chen, K. (2023). Comparative performance of three machine learning models in predicting influent flow rates and nutrient loads at wastewater treatment plants. ACS ES&T Water. https://doi.org/10.1021/acsestwater.3c00155