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Ju‐Young Shin
Observed names:
J.-Y. Shin,
Ju-Young Shin,
Ju-young Shin,
Juyoung Shin,
Shin, Ju-Young,
신주영
show all 6
Institution:
Kookmin University
Observed institutions:
Kookmin University,
Korea Meteorological Administration,
Yonsei University,
National Institute of Meteorological Sciences,
Masdar Institute of Science and Technology
ORCID:
0000-0002-1520-3965
Observed ORCID:
0000-0002-1520-3965
OpenAlex ID:
A5013530888
Probability distributions of wind speed in the UAE
2015
·
T.B.M.J. Ouarda
, C. Charron
, et al.
·
Energy Conversion and Management
·
233
Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change
2019
·
Jun-Haeng Heo
, Hyunjun Ahn
, et al.
·
Water
·
146
PDF
Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization
2020
·
Trang Thi Kieu Tran
, Taesam Lee
, et al.
·
Atmosphere
·
99
PDF
Regional quantile delta mapping method using regional frequency analysis for regional climate model precipitation
2020
·
Sunghun Kim
, Kyungwon Joo
, et al.
·
Journal of Hydrology
·
85
Heterogeneous mixture distributions for modeling wind speed, application to the UAE
2016
·
Ju-Young Shin
, Taha B.M.J. Ouarda
, et al.
·
Renewable Energy
·
70
Spatial and temporal variations in rainfall erosivity and erosivity density in South Korea
2019
·
Ju-Young Shin
, Taereem Kim
, et al.
·
CATENA
·
63
Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition
2017
·
Taereem Kim
, Ju-Young Shin
, et al.
·
Journal of Hydrology
·
58
Monthly Precipitation Forecasting with a Neuro-Fuzzy Model
2012
·
Changsam Jeong
, Ju-Young Shin
, et al.
·
Water Resources Management
·
53
Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management
2019
·
Ju-Young Shin
, Kyu Rang Kim
, et al.
·
Agricultural and Forest Meteorology
·
51
The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models
2019
·
Taereem Kim
, Ju-Young Shin
, et al.
·
Water
·
51
PDF
Allergenic Pollen Calendar in Korea Based on Probability Distribution Models and Up-to-Date Observations
2020
·
Ju-Young Shin
, Mae Ja Han
, et al.
·
Allergy Asthma and Immunology Research
·
37
PDF
A new approach for river network classification based on the beta distribution of tributary junction angles
2019
·
Kichul Jung
, Ju-Young Shin
, et al.
·
Journal of Hydrology
·
35
Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection
2020
·
Taereem Kim
, Ju‐Young Shin
, et al.
·
Water Resources Research
·
33
Meta-heuristic maximum likelihood parameter estimation of the mixture normal distribution for hydro-meteorological variables
2013
·
Ju-Young Shin
, Jun-Haeng Heo
, et al.
·
Stochastic Environmental Research and Risk Assessment
·
32
The Spatial and Temporal Structure of Extreme Rainfall Trends in South Korea
2017
·
Younghun Jung
, Ju-Young Shin
, et al.
·
Water
·
30
PDF
High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea
2022
·
Ju-Young Shin
, Byunghoon Min
, et al.
·
International Journal of Biometeorology
·
29
PDF
Heterogeneous Mixture Distributions for Modeling Multisource Extreme Rainfalls*
2015
·
Ju-Young Shin
, Taesam Lee
, et al.
·
Journal of Hydrometeorology
·
28
A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property
2018
·
Ju-Young Shin
, Changsam Jeong
, et al.
·
Energies
·
27
PDF
Outdoor thermal stress changes in South Korea: Increasing inter-annual variability induced by different trends of heat and cold stresses
2021
·
Ju-Young Shin
, Misun Kang
, et al.
·
The Science of The Total Environment
·
25
PDF
Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018
2019
·
Ju-Young Shin
, Yonghun Ro
, et al.
·
Advances in Meteorology
·
24
PDF
Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method
2020
·
Hanbeen Kim
, Ju-Young Shin
, et al.
·
Advances in Water Resources
·
23
Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms
2020
·
Ju-Young Shin
, Bu-Yo Kim
, et al.
·
Remote Sensing
·
23
PDF
Bias correction of RCM outputs using mixture distributions under multiple extreme weather influences
2018
·
Ju-Young Shin
, Taesam Lee
, et al.
·
Theoretical and Applied Climatology
·
23
Event-Based Heat-Related Risk Assessment Model for South Korea Using Maximum Perceived Temperature, Wet-Bulb Globe Temperature, and Air Temperature Data
2020
·
Misun Kang
, Kyu Rang Kim
, et al.
·
International Journal of Environmental Research and Public Health
·
21
PDF
Intensity-duration-frequency relationship of WBGT extremes using regional frequency analysis in South Korea
2020
·
Ju-Young Shin
, Kyu Rang Kim
, et al.
·
Environmental Research
·
18
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Works count:
59
Citation count:
1,462
H-index:
23
I10-index:
31
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