Minyeob Jeong | Flood Prediction | Best Researcher Award

Published on by Civil Engineering Awards

Dr Minyeob Jeong | Flood Prediction | Best Researcher Award

Postdoctoral researcher at University of Ulsan, South Korea

Minyeob Jeong is a dedicated researcher in Civil Engineering with a specialization in flood prediction and hydrological modeling. He obtained his Bachelor’s, Master’s, and Ph.D. degrees from the University of Seoul, focusing on advanced methodologies for rainfall-runoff predictions. With extensive experience as a researcher and postdoctoral scholar, his work integrates deep learning with hydrological models to improve flood forecasting accuracy. His research contributions have been widely published in esteemed journals, covering topics like hydroinformatics, soil erosion, and watershed data assimilation. His expertise in nonlinear hydrological response functions and dynamic wave modeling has significantly impacted water resource management.

PROFESSIONAL PROFILE

Google Scholar

Orcid

Scopus

EDUCATION 🎓

  • High School (2013): Graduated from Pohang Yeongil High School
  • Bachelor’s (2019): Civil Engineering, University of Seoul
  • Ph.D. (2023): Civil Engineering, University of Seoul
  • Language Proficiency: TOEIC Score – 845

EXPERIENCE 🏗️

  • 2019 – 2023: Researcher, Department of Civil Engineering, University of Seoul
  • 2023 – Present: Postdoctoral Researcher, Department of Civil and Environmental Engineering, University of Seoul

AWARDS AND HONORS 🏆

  • Recognized for outstanding contributions in hydrological research
  • Published multiple high-impact research papers in top-tier journals
  • Recipient of research excellence awards at Korea Water Resources Association Conferences
  • Active contributor to advancing flood prediction and hydrological modeling

RESEARCH FOCUS 🔬

Minyeob Jeong’s research centers on integrating hydrological modeling, deep learning, and data assimilation for accurate flood prediction. His work explores nonlinear rainfall-runoff relationships, soil erosion modeling, and watershed data integration. By leveraging machine learning techniques alongside traditional hydrological models, his studies have improved the precision of flood forecasting systems, offering innovative solutions for water resource management and disaster prevention.

PUBLICATION TOP NOTES 📚

  • Instantaneous physical rainfall–runoff prediction technique using power-law relationships
  • Surface runoff hydrograph derivation using dynamic wave-based instantaneous unit hydrograph
  • Effects of Soil Particle Size on Relationship Between Microtopography Roughness and Soil Erosion
  • Flood prediction using nonlinear instantaneous unit hydrograph and deep learning
  • High flow prediction model integrating physical and deep learning approaches
  • On the nonlinearity of the catchment instantaneous response function
  • Physical and Deep Learning Hybrid Flood Forecasting Model for Ungauged Watersheds
  • A numerical study of single N-type tsunami drawdown processes
  • Rainfall-runoff hydrograph prediction using dynamic wave-based instantaneous unit hydrograph
  • Microtopography Effects on Rainfall and Sediment Runoff in Arid and Semi-Arid Regions

CONCLUSION 🌍

Minyeob Jeong is a forward-thinking researcher committed to enhancing hydrological modeling through cutting-edge computational techniques. His innovative approaches to flood prediction and water resource management contribute significantly to environmental sustainability and disaster risk mitigation.

Zhenlin Chen | Environmental Modeling | Best Researcher Award

Published on by Civil Engineering Awards

Mr. Zhenlin Chen | Environmental Modeling | Best Researcher Award

Zhenlin (Richard) Chen is a Ph.D. candidate in Energy Science Engineering at Stanford University, where his research focuses on energy systems, environmental sustainability, and advanced methane detection technologies. With a background in Environmental Science and Information Science, Zhenlin’s work bridges energy, technology, and policy. He has contributed to numerous publications and collaborations, often focusing on leveraging data-driven models and advanced monitoring technologies to address environmental challenges. Passionate about climate action, Zhenlin combines his technical expertise with a commitment to advancing sustainability practices in the energy sector.

Profile

Google Scholar

Education

Zhenlin (Richard) Chen is pursuing a Ph.D. in Energy Science Engineering at Stanford University, having previously completed an M.S. in Civil and Environmental Engineering. He also holds a Master of Professional Studies (M.P.S.) in Information Science and a Bachelor of Science (B.S.) in Environment and Sustainability from Cornell University. Zhenlin’s academic journey includes a series of honors and a strong GPA (3.74/4.0). Relevant coursework includes life cycle assessment, applied mathematics, and energy systems fundamentals, laying the foundation for his interdisciplinary research in energy science and sustainability.

Experience

Zhenlin’s experience spans academia, research, and industry. As a research associate at Stanford’s Environmental Assessment and Optimization Group, he contributed to methane monitoring technologies and collaborated with various stakeholders, including industry leaders. His work involved experimental design, data collection, and the use of machine learning for data analysis. Additionally, Zhenlin’s internship at MioTech focused on ESG data analysis, and he co-founded Young Sustainable Impact in Greater China, where he led a team to tackle sustainability challenges and foster innovation.

Research Focus

Zhenlin’s research focuses on advancing technologies for environmental monitoring, specifically methane emissions, and energy sector optimization. At Stanford, he develops frameworks using large language models (LLMs) for key data extraction, aiming to improve environmental data accessibility for climate modeling. He also works on optimizing methane detection technologies and analyzing energy systems. Zhenlin’s interdisciplinary approach blends environmental science, machine learning, and policy to drive innovations in energy sustainability and greenhouse gas mitigation.

Publication Top Notes

  • Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions 📄
  • Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation 🌍
  • Evaluating the Sustainable Development Goals within Spatial Planning for Decision-Making 🏙️
  • Single-Blind Test of Nine Methane-Sensing Satellite Systems 🛰️
  • Comprehensive Evaluation of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation 🌎
  • AI-Driven Environmental Data Extraction for Energy Sector Assessment 🤖