Project-5 Analyzing the Water Cycle in Different Time Scales Using Bayesian Inferences and Data Assimilation Methods
What is the impact of human-driven changes in land cover on the terrestrial water cycle?
Hyunglok Kim
Terrestrial Water CycleHydrologyRemote SensingBayesian InferenceMachine LearningDeep Learning
Email Google Scholar LinkedIn Instagram Github ResearchGate OrcidCurrent Project Posts
2021
Project-4 Deep Learning and Bayesian Inference to Characterize Spatially Continuous Global Soil Moisture Error Patterns
How can the Bayesian inference be used to intepret and predict the error characteristics of global-scale soil moisture?
Project-3 Deep Learning Approach to Predict Peak Floods and Evaluation of Socioeconomic Vulnerability to Flood Events
How can the deep learning approach help predict peak flow and time to peak?
Project-2 Assimilating Satellite-based Soil Moisture Data into Land Surface Models Based on Parallel Computing
How can satellite-retreived soil moisture data improve land surface models?
Project-1 Developing a Method for a Satellite-based Soil Moisture Retrieval Algorithm Using GPS Signals
How can GPS satellites help us to monitor water in topsoil layers?