Doctor at China University of Geosciences (Wuhan), China
Aohan Jin is a dedicated PhD candidate at the School of Environmental Studies, China University of Geosciences (Wuhan). His research primarily focuses on groundwater flow dynamics and its influence on solute and heat transport within porous and fractured media. With a passion for hydrological modeling and advanced machine learning techniques, Aohan has made significant strides in his academic journey, publishing six SCI papers and contributing as a reviewer for esteemed journals like the Journal of Hydrology and Journal of Hydrologic Engineering.
Profile
Google scholar
Education🎓
Aohan is pursuing his PhD in Environmental Studies at the China University of Geosciences (CUG), Wuhan. His academic foundation is built upon rigorous training in numerical modeling and hydrology, enabling him to tackle complex environmental challenges.
Experience🔬
Aohan has collaborated with the National Natural Science Foundation of China (Grant No. 42222704), bringing valuable insights into hydrological systems. Beyond academic research, he has contributed to four consultancy and industry projects, showcasing his ability to translate scientific knowledge into practical applications.
Research Interests📚
Aohan’s research interests revolve around:
- Numerical modeling for groundwater and solute transport.
- Hydrological modeling using advanced computational techniques.
- Applications of machine learning in hydrology, particularly for forecasting and simulation.
Awards and Recognitions🏆
While Aohan’s focus remains on academic excellence, he is striving to expand his accolades by participating in competitive awards, such as the Best Researcher Award. His innovative work, including the novel TSDRF framework for hydrological forecasting, highlights his commitment to pushing the boundaries of hydrological science.
Publications📖
Comparative performance assessment of physical-based and data-driven machine-Learning models for simulating streamflow: a case study in three catchments across the US
- Authors: A Jin, Q Wang, H Zhan, R Zhou
- Journal: Journal of Hydrologic Engineering, 29(2), 05024004
- Citations: 8
- Year: 2024
Revisiting simplified model of a single-well push–pull test for estimating regional flow velocity
- Authors: Q Wang, A Jin, H Zhan, Y Chen, W Shi, H Liu, Y Wang
- Journal: Journal of Hydrology, 601, 126711
- Citations: 3
- Year: 2021
A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting
- Authors: R Zhou, Y Zhang, Q Wang, A Jin, W Shi
- Journal: Journal of Hydrology, 634, 131128
- Citations: 2
- Year: 2024
Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework
- Authors: A Jin, Q Wang, R Zhou, W Shi, X Qiao
- Journal: Journal of Hydrologic Engineering, 29(5), 04024026
- Citations: 1
- Year: 2024
Interpretable multi-step hybrid deep learning model for karst spring discharge prediction: Integrating temporal fusion transformers with ensemble empirical mode decomposition
- Authors: R Zhou, Q Wang, A Jin, W Shi, S Liu
- Journal: Journal of Hydrology, 645, 132235
- Citations: 0 (not yet cited)
- Year: 2024
A novel four phase slug single-well push–pull test with regional flux: forward modeling and parameter estimation
- Authors: A Jin, Q Wang, H Zhan
- Journal: Journal of Hydrology, 630, 130705
- Citations: 0 (not yet cited)
- Year: 2024
Conclusion🌟
Dr. Aohan Jin’s innovative approach to integrating machine learning with hydrological modeling is a significant advancement in the field of environmental studies. His ability to develop new frameworks and file patents demonstrates his potential to make substantial contributions to hydrology and environmental science. While there are areas to expand, particularly in terms of ongoing projects and academic leadership, his achievements thus far make him a strong candidate for the Best Researcher Award.