We investigate the impact of climate changes on regional to the global scale water cycle with remote sensing, land surface models, and AI/machine learning technics.
Our goal is to understand how water movement across the land surface relates to broader-scale behavior, and to improve the accuracy of natural disaster and water resource predictions by applying remote sensing and machine learning techniques in the context of the climate crisis.
우리 수문 원격탐사 인공지능 연구실(HydroAI)은 기후변화/인간 활동에 의한 환경의 변화와 자연재해/수자원 예측 및 분석을 위해 인공위성 및 수치모델 자료와 AI/기계학습 기술을 적극 활용합니다. 전 지구적 규모로 발생하는 자연재해(가뭄, 홍수, 산불, 황사 등) 및 수자원을 위성 영상을 통해 더욱 신속하고 정확하게 예측하는 것은 기후변화에 대응한 가장 중요한 연구 분야로 주목받고 있습니다. 우리 연구실에서 현재 진행하고 있는 프로젝트는 여기에서 확인이 가능합니다.
• 위성 원격 탐사, 수치모델, 기후변화, 환경 분야 Domain Scientist로 AI/기계학습 방법론 적용 연구에 대한 열정을 가지고 있는 학부생 및 대학원 진학 예정 학생을 환영합니다.
• (2025년도 하반기) 차세대 위성/드론을 이용한 환경 원격탐사에 경험 및 관심이 있는 학생을 모집합니다.(석/박 통합 TO 메일로 문의)
• 딥러닝 방법론 중 physics-informed neural networks (PINNs), partial convolution neural network (PCNN), super-resolution GAN (SRGAN), reinforcement learning (RL) 등을 활용한 위성 이미지 분석 및 정확도 향상, long short-term memory (LSTM), gated recurrent unit (GRU), Transfer Learning (TL) 을 활용한 각종 자연재해 예측, 지표 모델링 위한 loss function 개발에 관심이 있는 학생들을 환영합니다.
• 석사/박사 (한국)학생의 경우 여름/겨울 방학 기간에 미국/유럽 연구 기관 및 대학교에서 방문 연구 기회를 제공합니다. 배움에 열정이 있는 모든 학생을 환영합니다. 관심 있는 학생은 Contact 페이지를 통해 연락 바랍니다.
• If you are passionate about hydrology, remote sensing, and machine learning applications in Earth sciences, please feel free to contact me.
• Particularly, we are looking for students interested in satellite image analysis and accuracy improvement using deep learning (DL) methodologies such as PINN, PCNN, GAN, SRGAN, predictions of various natural disasters using LSTM, GRU, and the development of loss functions for environmental data analysis. We welcome students who have always been interested in applying AI/ML/DL methodologies in the civil/environmental field but couldn't learn them, as well as students who majored in AI/ML/DL but wanted to contribute to society through research in climate change and natural disaster prediction.
• We are currently interveiwing MS and one Ph.D. students who are interested in utilizing machine learning techniques to address water-related and climate change issues. Before reaching out to me, you can visit this page for what opportunities are currently available. I'm excited to work with the next generation of researchers in this exciting and rapidly evolving field.
We use shallow machine learning and deep learning approaches, along with the application of Bayesian theorem, to predict natural phenomena and improve the accuracy of our data.
We utilize various Earth observing satellite systems to monitor the dynamics of Earth's systems and gain insight into the global water cycle.
We utilize land surface models at various scales to comprehend physical processes and examine how the land surface and atmosphere interact with each other on the ground.
We integrate data from ground, satellites, and land surface models to improve the quality and quantity of the data, leading to a better understanding of Earth's processes.
Our research projects involve the study of the Earth's complex processes, and the interactions between the Earth's various components. The complexity, scale, and nonlinearity of these processes make machine learning (ML) an ideal tool for advancing our understanding and predictive capabilities. Earth science generates massive amounts of data from various sources, including satellite observations, ground-based measurements, and numerical simulations. ML algorithms can efficiently process, analyze, and extract valuable insights from these large, diverse, and complex datasets. In addition, we are devoted to understanding the uncertainty of the data using a Bayesian ML approach.
We use various shallow and deep ML approaches to predict water resources, natural disasters (e.g., floods and droughts), data assimilation, and explainable and Bayesian ML for the uncertainty analysis. Please check our relevant research projects below.
Bayesian machine learning
Streamflow prediction over ungaged regions
Floods and droughts prediction
Explainable machine learning for the climate change analysis
We use Earth observing satellite systems to collect comprehensive data sets, allowing us to monitor global changes in the environment and improve our understanding of climate, land use, natural disasters, and other environmental processes. We are currently focused on the following research projects.
Global-scale water cycle analysis
Spatial and temporal resolution improvments
Algorithm developments for new data
Data uncertainty quantification and prediction
Land surface models (LSMs) are computer models that simulate land surface processes, such as energy and water fluxes, carbon and nutrient cycles, and vegetation dynamics. They help researchers understand and predict how land surface processes respond to environmental changes like climate change and land use change. They operate at different scales: microscale, mesoscale, and global scale.
We use the Land Information System (LIS) to run various land surface models and combine them with other types of observational data to provide a comprehensive view of land surface processes. Please check our relevant research topics below.
Impact of human activities on the water cycle
Data assimilation (integration)
Global-scale water balance analysis
Our paper, titled “Assessing Land Cover-Specific Responses to Flash Droughts in the Mississippi River Basin,” published in Journal of Environmental Management (IF: 8, top 9%) by Ms. Bakar (visiting scholar) investigates how different land cover types respond to rapidly developing drought events from 2000 to 2022. By utilizing the Standardized Anomaly-based Precipitation Evapotranspiration Index (SAPEI) for drought detection and Gross Primary Production (GPP) data to assess vegetation recovery, we identified 315 flash droughts and analyzed the 10 most severe cases. Recovery times varied significantly, ranging from 8 to 120 days, with the most prolonged recovery periods observed during extreme drought years such as 2006, 2012, and 2022. Our results show that forested areas recover faster, whereas rain-fed agricultural lands exhibit the highest vulnerability to sudden moisture loss, particularly in the drier Upper Mississippi River Basin (MRB), where intensive farming practices exacerbate drought impacts. These findings underscore the necessity for improved drought management strategies, including optimized water resource allocation and the development of drought-resistant crops, to enhance resilience in vulnerable regions.
Link
Our research team is committed to staying up-to-date in the field of Earth science by attending at least two international conferences each year, while also contributing to hydrologic research fields through publishing in top-tier journals.
The most recent symposium paper (HydroML 2023 at Lawrence Berkeley National Laboratory)
This study highlights the significance of characterizing errors in satellite-basedsoil moisture (SM) data, crucial for numerous Earth Science and EnvironmentalEngineering applications.
If you have a keen interest in the intersection of climate change and its impact on hydrological research fields, I encourage you to consider pursuing a Master's, PhD, or postdoctoral position. By delving deeper into this critical area of study, you can play an essential role in addressing the world's most pressing environmental challenges and help safeguard our water resources, ecosystems, and communities. Your dedication and expertise can significantly contribute to the development of sustainable solutions and innovative approaches to hydrological research. Embark on this exciting journey and become part of the passionate community of scientists working towards a more resilient and environmentally responsible future.
