Yonsei University · 環境科学
Professor Jun-Haeng Heo's research lab specializes in hydrological modeling, flood forecasting, and reservoir system optimization under climate variability and uncertainty. The lab focuses on developing advanced statistical and machine learning models—such as quantile mapping, SARIMA, and artificial neural networks—for bias correction of climate and hydrological data, as well as short-term flood and water level forecasting. A key research direction involves integrating large-scale climate indices and continuous hydrological simulations to improve the reliability of reservoir operation and flood risk management in complex river basins.
Figures are computed from collected data and may differ slightly.
The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping m
A monthly operating rule for single-reservoir operation is developed in this study. Synthetic inflow data over 100 years are generated by using a time series model, AR(1), and piecewise-linear operating rules consisting of 4 and 5 linear lines are found using the implicit stochastic optimization method. In order to consider multiobjective functions in reservoir system operation, a multiobjective genetic algorithm (NSGA-II) is adopted to obtain the optimization results. The search space of NSGA-I
Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For
The proper assessment of design flood is a major concern for many hydrological applications in small urban watersheds. A number of approaches can be used including statistical approach and the continuous simulation and design storm methods. However, each method has its own limitations and assumptions being applied to the real world. The design storm method has been widely used for a long time because of the simplicity of the method, but three critical assumptions are made such as the equality of
The purpose of this study is to evaluate the impacts of the upstream Soyanggang and Chungju multi-purpose dams on the frequency of downstream floods in the Han River basin, South Korea. A continuous hydrological model, SWAT (Soil and Water Assessment Tool), was used to individually simulate regulated and unregulated daily streamflows entering the Paldang Dam, which is located at the outlet of the basin of interest. The simulation of the regulated flows by the Soyanggang and Chungju dams was cali
This study develops hourly water level forecasting models with lead-times of 1 to 3 h using an artificial neural network (ANN) for Anyangcheon stream, one of the major tributaries of the Han River, South Korea. To consider the backwater effect from this river, an enhanced tributary water level forecasting model is proposed by adding multiple water level data on the main river as input variables into the conventional ANN structure which often uses rainfall and upstream water level data. Four type
Abstract Artificial neural networks (ANNs) have been extensively used to forecast monthly precipitation for water resources management over the past few decades. Efforts to produce more accurate and stable forecasts face ongoing challenges as the so‐called single‐ANN (S‐ANN) approach has several limitations, particularly regarding uncertainty. Many attempts have been made to deal with different types of uncertainties by applying ensemble approaches. Here, we propose a new ANN ensemble model (ANN
To improve our capacity to use available wind speed data, it is necessary to develop a new statistical temporal downscaling method that uses one or a few input variables of any temporal scale for mean wind speed data to obtain wind statistics at finer temporal resolution. In the present study, a novel statistical temporal downscaling method for wind speed statistics and probability distribution is proposed. The proposed method uses the temporal structure to downscale the wind speed statistics to
The spatial and temporal structures of extreme rainfall trends in South Korea are investigated in the current study. The trends in the annual maximum rainfall series are detected and their spatial distribution is analyzed. The scaling exponent is employed as an index representing the temporal structure. The temporal structure of the annual maximum series is calculated and spatially analyzed. Subsequently, the block bootstrap based Mann-Kendall test is employed detect the trend in the scaling exp
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