• KI-HopE-De - AI-based flood prediction in small river basins in Germany (2025-current)

• AI-based detecting and quantification of floating macroplastic litter in rivers and urban waterways in the Netherlands (2020-2025)

• Optimal reservoir operation in the upper and middle reaches of the Yangtze River, China, considering power generation, flood control, and navigation demands (2017–2020)

My primary research interests are in emerging problems related to environment monitoring, smart water management, and artificial intelligence research, including:

• Artificial Intelligence (AI)
  • Deep learning, and machine learning
  • Unsupervised learning, and semi-supervised learning
  • Self-supervised learning, and contrastive learning
  • Time Series Forecasting
  • Computer vision (e.g., image classification, object detection, and image segmentation)
  • Transfer learning
  • Explainable AI
• Smart water management
  • Flood forecasting
  • Optimal operation of hydropower reservoirs
  • Multi-objective optimization
  • Genetic Algorithm
  • Hydroinformatics
  • Hydrodynamic simulation
• Environment monitoring
  • Floating macroplastics monitoring
  • Earth observation
  • Remote sensing

• Delft University of Technology (TUD) (2020-2025)
  • Ph.D., AidroLab, AI for sustainable water management, the Netherlands
  • Focus: Artificial Intelligence and Environment monitoring
• Huazhong University of Science and Technology (2017-2020)
  • M.Sc., School of Hydropower and Information Engineering, China
  • Focus: Optimized scheduling of hydropower for reservoir 
• Harbin Engineering University (2013-2017)
  • B.S., College of Shipbuilding Engineering, China
  • Focus: Port & Waterway and Coastal Engineering

Doctoral Thesis

• Jia, T. (2025). Deep learning-based Methods for Detecting and Quantifying floating litter in Riverine Environments [Dissertation (TU Delft), Delft University of Technology].

Journal Papers

• Yildizli, T.*, Jia, T., Langeveld, J., & Taormina, R. (2026). Self-supervised learning for multi-label sewer defect classification. Automation in Construction. [PDF] [Code]

• Jia, T.*, Taormina, R., de Vries, R., Kapelan, Z., van Emmerik, T. H. M., Vriend, P., & Okkerman, I. (2025). A semi-supervised learning-based framework for quantifying litter fluxes in river systems. Water Research. [PDF] [Code]

• van Emmerik, T. H. M.*, Janssen, T. W., Jia, T., Bui, T.-K. L., Taormina, R., Nguyen, H.-Q., & Schreyers, L. J. (2025). Plastic pollution and water hyacinths consistently co-occur in the lower Saigon river. Environmental Research: Water. [PDF] [Code]

• Chen, G.*, Wu, X., Hu, L., Chi, Y., Jia, T., & Luo, Y. (2025). Numerical Analysis of 3D Slope Stability in a Rainfall-Induced Landslide: Insights from Different Hydrological Conditions and Soil Layering. Water. [PDF]

• Jia, T., Yu, J., Sun, A., Wu, Y., Zhang, S., & Peng, Z.* (2025). Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment. Journal of Environmental Management. [PDF] [Code]

• Jia, T.*, de Vries, R., Kapelan, Z., van Emmerik, T. H. M., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. Water Research. [PDF] [Code]

• Jia, T., Peng, Z.*, Yu, J., Piaggio, A. L., Zhang, S., & de Kreuk, M. K. (2024). Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method. Science of The Total Environment. [PDF] [Code]

• Wu, Y., Ma, X., Guo, G., Jia, T., Huang, Y., Liu, S.*, Fan, J., & Wu, X. (2024). Advancing Deep Learning-Based Acoustic Leak Detection Methods Towards Application for Water Distribution Systems from a Data-centric Perspective. Water Research. [PDF]

• Jia, T.*, Vallendar, A., de Vries, R., Kapelan, Z., & Taormina, R. (2023). Advancing Deep Learning-based Detection of Floating Litter using a Novel Open Dataset. Frontiers In Water. [PDF] [Code]

• Jia, T.*, Kapelan, Z., de Vries, R., Vriend, P., Peereboom, E. C., Okkerman, I., & Taormina, R. (2023). Deep learning for detecting macroplastic litter in water bodies: A review. Water Research. [PDF]

• Chen, G., Zhang, K.*, Wang, S., & Jia, T. (2023). PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale. Environmental Modelling & Software. [PDF] [Code]

• Jia, T., Qin, H.*, Yan, D., Zhang, Z., Liu, B., Li, C., Wang, J., & Zhou, J. (2019). Short-term multi-objective optimal operation of reservoirs to maximize the benefits of hydropower and navigation. Water. [PDF]

Conference papers or abstract

• Yildizli, T., Jia, T., Langeveld, J., & Taormina, R. Self-supervised learning approach for automatic sewer defect detection, 13th Urban Drainage Modelling Conference, Innsbruck, Austria, September, 2025.
• Jia, T., Taormina, R., de Vries, R., Kapelan, Z., van Emmerik, T. H. M., Vriend, P., & Okkerman, I. Quantifying Floating Litter Fluxes with a Semi-Supervised Learning-Based Framework, EGU25 (European Geosciences Union) Conference, Vienna, Austria, April 2025.
• Yildizli, T., Jia, T., Langeveld, J., & Taormina, R. Self-Supervised Learning Approach for Sewer Defect Detection, 6th International Conference on Water Economics, Statistics and Finance and 10th Leading Edge Conference for Strategic Asset Management (LESAM), Pafos, Cyprus, April, 2025.
• Yildizli, T., Jia, T., Langeveld, J., & Taormina, R. Self-supervised learning approach for automatic sewer defect detection, 16th International Conference on Urban Drainage 2024, Delft, the Netherlands, June 2024.
• Jia, T., de Vries, R., Kapelan, Z., & Taormina, R. Detecting Floating Macroplastic Litter with Semi-Supervised Deep Learning, EGU24 (European Geosciences Union) Conference, Vienna, Austria, April 2024.
• Jia, T., de Vries, R., Kapelan, Z., & Taormina, R. Detecting Floating Macroplastic litter with Semi-supervised Deep Learning, AGU23 (American Geophysical Union) Conference, San Francisco, CA, the United States, December 2023.
• Vallendar, A., Jia, T., de Vries, R., Kapelan, Z., & Taormina, R. An open source dataset for Deep Learning-based visual detection of floating macroplastic litter, EGU23 (European Geosciences Union) Conference, Vienna, Austria, April 2023.
• Jia, T., de Vries, R., Kapelan, Z., & Taormina, R. A robust deep learning methodology to detect floating macro-plastic litter in rivers, EGU22 (European Geosciences Union) Conference, Vienna, Austria, May 2022.
• Jia T., Zhou J.*, & Liu X. A daily power generation optimized operation method of hydropower stations with the navigation demands considered, 1st International Symposium on Water System Operations, Beijing, China, 2018

Academic Conferences & forums

• Apr. 2024: The EGU General Assembly 2024 (EGU 2024), Vienna, Austria
• Dec. 2023: American Geophysical Union Fall Meeting 2023 (AGU 2023), San Francisco, USA
• Dec. 2023: 2nd Europe-China Eco-Environmental Forum for Young Scholars, Special topic on water environment
• Apr. 2023: The EGU General Assembly 2023 (EGU 2023), Vienna, Austria
• Dec. 2022: 1st Europe-China Eco-Environmental Forum for Young Scholars, Special topic on water environment
• Dec. 2022: Plastic research in the Netherlands: Perspectives on aquatic plastic monitoring (workshop), Wageningen, the Netherlands
• May. 2022: The EGU General Assembly 2022 (EGU 2022), Vienna, Austria
• Apr. 2019: Tsinghua University “治者论水” Doctoral Academic Forum, Beijing, China
• Oct. 2018: 1st International Symposium on Water System Operations (ISWSO 2018), Beijing, China

 Professional Membership

• American Geophysical Union Member
• EGU General Assembly Member

Editor for Journals

• Energy & Environment Nexus, Early Career Editor
• Remote Sensing, Guest Editor for a special issue

Reviewer for Journals

• Hydrology and Earth System Sciences
• Water Research
• Neurocomputing
• npj Computational Materials
• Environmental Science and Pollution Research
• Frontiers in Earth Science
• Microplastics and Nanoplastics
• IET Image Processing
• Signal, Image and Video Processing