Dr. Shijie Jiang
Department of Computational Hydrosystems (CHS)
Helmholtz-Zentrum für Umweltforschung - UFZ
Building 7.1, Room 412
What I do
My research focuses on transdisciplinary applications of advanced artificial intelligence techniques (e.g., explainable AI and computer vision) into geoscientific fields (specifically hydrology), including (1) interpretive machine learning for scientific discovery, (2) physics-informed deep learning, and (3) computer vision in hydrology. In the UFZ, I am associated with the project COMPOUNDX that aims to detect compound climate features of extreme impacts. The complete description of the project can be seen here.
|2017 - 2021||
National University of Singapore
Ph.D. in Hydrology (NUS-SUSTech Collaborative Ph.D. Program)
Thesis: New paradigms for application of intelligent techniques in hydrology: bridging monitoring, modeling, and understanding
|2013 - 2016||
Beijing Normal University, China
M.Sc. in Groundwater Science and Engineering
|2009 - 2013||
Wuhan University, China
B.Sc. in Agricultural Water Resources Engineering
- Jiang, S., Zheng, Y., Wang, C., & Babovic, V. (2022). Uncovering flooding mechanisms across the contiguous United States through interpretive deep learning on representative catchments. Water Resources Research, 58(1), e2021WR030185. DOI: 10.1029/2021WR030185
- Jiang, S., Zheng, Y., & Solomatine, D. (2020) Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning. Geophysical Research Letters, 47(13), e2020GL088229. DOI: 10.1029/2020GL088229
- Jiang, S., Babovic, V., Zheng, Y., & Xiong, J. (2019) Advancing opportunistic sensing in hydrology: a novel approach to measuring rainfall with ordinary surveillance cameras. Water Resources Research, 55(4), 3004–3027. DOI: 10.1029/2018WR024480 (This paper is highlighted as the Research Spotlight in Eos: Ordinary security cameras could keep an eye on rainfall).
- Jiang, S., Zheng, Y., Babovic, V., Tian, Y., & Han, F. (2018) A computer vision-based approach to fusing spatiotemporal data for hydrological modeling. Journal of Hydrology, 567, 25–40. DOI: 10.1016/j.jhydrol.2018.09.064
- Jiang, S., Zhai, Y., Leng, S., Wang, J., & Teng, Y. (2016) A HIVE model for regional integrated environmental risk assessment: A case study in China. Human and Ecological Risk Assessment, 22(4), 1002–1028. DOI: 10.1080/10807039.2015.1122510
- Tian, Y., Xiong, J., He, X., Pi, X., Jiang, S., Han, F.,& Zheng, Y. (2018) Joint operation of surface water and groundwater reservoirs to address water conflicts in arid regions: An integrated modeling study. Water, 10(8), 1105. DOI: 10.3390/w10081105
- Leng, S., Zhai, Y., Jiang, S., Lei, Y., & Wang, J. (2017) Water-environmental risk assessment of the Beijing–Tianjin–Hebei collaborative development region in China. Human and Ecological Risk Assessment, 23(1), 1002–1028. DOI: 10.1080/10807039.2016.1229119
- Interpretative deep learning in hydrological sciences: Inferring flood-generating mechanisms at continental scales. Presented at the 18th annual meeting of the Asia Oceania Geosciences Society (AOGS), August 1-6, 2021. Online.
- Equip deep learning with physical insights: Towards a symbiotic integration for hydrologic modeling. Presented at AGU Fall Meeting, December 1-17, 2020. Online.
- Hydrological observations and models in the era of big data. Presented at Graduate Student Research Symposium on Smart Water, May 25, 2019. Dalian, China. (invited)
- Hydrological modeling in the era of big data: A computer vision-based approach to fusing spatiotemporal data. Presented at AGU Fall Meeting, December 10–14, 2018. Washington D.C., USA.