Hyunje Yang

Mar. 2018 – Apr. 2023      National Institute of Forest Science (NIFoS)

                                                         Researcher; Forest Environment and Conservation Department

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                                                         [Government R&D Projects]

          Mar. 2021 – Apr. 2023        ● Establishment of Big Data and Integrated Utilization System for Flash Flood in Forest Watershed         

                                                           – Develop flash flood forecasting models using time series flood datasets and machine learning.

                                                           – Find the optimal devices and algorithms for flash flood warning systems suited to forested areas.

 

          Mar. 2021 – Apr. 2023        ● Development of Forest Water Management Technology to Reduce Stream Depletion and

                                                             Nonpoint Source Pollution

                                                           – Monitor and collect data on changes in water quality after forest fire and forest thinning as

                                                              the preliminary stage for analysis.    

 

          Mar. 2018 – Apr. 2023        ● Quantification and Improvement of Forest Water Yield for Sustainable Supply of Freshwater         

                                                           – Calibrate interest parameters and analyze their uncertainties on physical hydrological

                                                              simulation model via Bayesian approach and Generalized Likelihood Uncertainty

                                                              Estimation (GLUE), using in situ measurement of the water cycle in different forest stands.

                                                           – Collect hydrological monitoring data for hydrograph analysis; compared various estimation methods                                                                      to quantify the baseflow most rationally from streamflow in forested catchments.

                                                           – Estimate the hydraulic properties and prediction uncertainties of soil based on a national

                                                              scale spatial big dataset using machine learning models; examined the influence of

                                                              environmental features on soil properties through sensitivity analysis.

                                                                ※ Selected as a 10-year national R&D project with a $ 2.8 M grant/year

 

          Mar. 2018 – Apr. 2023        ● Long-term Monitoring of Flow Characteristics in Different Forest Stands and Locations                 

                                                           – Collect the long-term water level and meteorological data from 52 sites and compare their

                                                              hydrological characteristics.

                                                           – Evaluate the effect of forest thinning on the forest water cycle changes based on

                                                              double mass curves and nonparametric statistics.