You can also find my papers in my Google Scholar.
Journal Papers
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zheng, T., Zhou J., Liu, L., Li, L., Jiang, W., Jia, T., & Xu, Y. (2019). Pumped Storage Unit Fault Correlativity Analysis and Research Based on Data Mining. Large Electric Machine and Hydraulic Turbine.
Jia, T., Xia, H., & Xu, H. (2015). Multinodal unmanned hydrological surveillance ship system based on Hadoop platform. Heilongjiang Water Resources.
Conference papers or abstract
Ehret, U., Loritz, R., Bondy, J., Demuth, N., Dolich, A., Hollborn, S., Jia, T., et al. ML-based Flood Forecasting for Small Catchments in Germany: The KI-HOPE-DE project, The 2025 Annual Meeting of the European Meteorological Society (EMS), Ljubljana, Slovenia, September 2025.
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
*: corresponding author
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].
Datasets
- Detection of floating litter in freshwater bodies using deep learning
The TU Delft-Green Village dataset (with labels for image classification tasks) [Link] [Paper]
The TU Delft-Green Village dataset (with annotations for object detection tasks) [Link] [Paper]
The Oostpoort dataset (with annotations for object detection tasks) [Link] [Paper]
The Amsterdam dataset (with annotations for object detection tasks) [Link] [Paper]
The Groningen dataset (with annotations for object detection tasks) [Link] [Paper]
The Wageningen UR-Ho Chi Minh City dataset (with annotations for object detection tasks) [Link] [Paper]
The TU Delft - Ho Chi Minh City dataset (with annotations for object detection tasks) [Link]
- Detection of the interaction between particles and biomass in biological wastewater treatment process with deep learning