• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of The Geomorphological Association of Korea
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• v.20 no.3
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• pp.41-53
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• 2013

This research was carried out to classify erosion shapes and sea-cliff erosion rates were estimated through periodic field survey in Dundu-ri, Anmyeondo. Based on the results of field measurements using the datum-point, the annual sea-cliff erosion rate was estimated about 25~102cm/yr by point. The erosion rate gradually increases from spring to summer, but tends to decrease slightly in autumn. Specifically, the erosion rate between June and July indicated a rather decreasing trend, but showed a sharp increase between July and September. This was attributed to erosion that proceeds more rapidly than during other periods due to severe rainstorms in summer that had a direct impact on the study area as well as storm surges caused by hurricanes. Afterwards, the sea-cliff erosion rate gradually decreased in autumn, but reflected an increasing trend again from December to January. This was attributed to the mechanical weathering that actively progresses as bed rocks on the sea-cliff undergo repeated freezing and thawing in winter. The seacliff in Dundu-ri is divided into three types according to the erosion shape. First, Type A is observed in the sea-cliff composed of the same bed rocks and hard rock stratum. Second, Type B is found in the sea-cliff with a relatively gentler slope compared to Type A, since weathering material including soil is formed on the surface of the sea-cliff consisting of the same bed rocks and hard rock stratum. Lastly, Type C is observed in the sea-cliff where hard rock stratum is mixed with soft rock stratum. In this case, the soft rock stratum slumps and erodes first by precipitation and wave energy, followed by additional slumping of the exposed hard rock stratum.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1589-1597
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• 2021

Submarine groundwater discharge (SGD) in coastal areas is gaining importance as a major transport route that bring nutrients and trace metals into the ocean. This paper describes the analysis of the seasonal changes and spatiotemporal characteristicsthrough the modeling monthly SGD for 35 years from 1986 to 2020 for the Nakdong river basin. In this study, we extracted 210 watersheds and SGD estimation points using the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model). The average annual SGD of the Nakdong River basin was estimated to be 466.7 m²/yr from the FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) recharge data of 10 km which is the highest resolution global model applicable to Korea. There was no significant time-series variation of SGD in the Nakdong river basin, but the concentrated period of SGD was expanded from summer to autumn. In addition, it was confirmed that there is a large amount of SGD regardless of the season in coastal area nearby large rivers, and the trend has slightly increased since the 1980s. The characteristics are considered to be related to the change in the major precipitation period in the study area, and spatially it is due to the high baseflow-groundwater in the vicinity of large rivers. This study is a precedentstudy that presents a modeling technique to explore the characteristics of SGD in Korea, and is expected to be useful as foundational information for coastal management and evaluating the impact of SGD to the ocean.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.533-544
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• 2018

Rainfall is one of the most important meteorological variables in meteorology, agriculture, hydrology, natural disaster, construction, and architecture. Recently, satellite remote sensing is essential to the accurate detection, estimation, and prediction of rainfall. In this study, the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) product, a composite rainfall information based on Global Precipitation Measurement (GPM) satellite was evaluated with ground observation data in the inland of Korea. The Automatic Weather Station (AWS)-based rainfall measurement data were used for validation. The IMERG and AWS rainfall data were collocated and compared during one year from January 1, 2016 to December 31, 2016. The coastal regions and islands were also evaluated irrespective of the well-known uncertainty of satellite-based rainfall data. Consequently, the IMERG data showed a high correlation (0.95) and low error statistics of Bias (15.08 mm/mon) and RMSE (30.32 mm/mon) in comparison to AWS observations. In coastal regions and islands, the IMERG data have a high correlation more than 0.7 as well as inland regions, and the reliability of IMERG data was verified as rainfall data.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.21-33
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• 2019

This study performed to simulate the watershed storm runoff using data of S-band dual-polarization radar rain, GPM (Global Precipitation Mission) satellite rain, and observed rainfall at 21 ground stations operated by KMA (Korea Meteorological Administration) respectively. For the 3 water level gauge stations (Sancheong, Changchon, and Namgang) of NamgangDam watershed ($2,293km^2$), the KIMSTORM2 (KIneMatic wave STOrm Runoff Model2) was applied and calibrated with parameters of initial soil moisture contents, Manning's roughness of overland and stream to the event of typhoon CHABA (82 mm in watershed aveprage) in $5^{th}$ October 2016. The radar and GPM data was corrected with CM (Conditional Merging) method such as CM-corrected Radar and CM-corrected GPM. The CM has been used for accurate rainfall estimation in water resources and meteorological field and the method combined measured ground rainfall and spatial data such as radar and satellite images by the kriging interpolation technique. For the CM-corrected Radar and CM-corrected GPM data application, the determination coefficient ($R^2$) was 0.96 respectively. The Nash-Sutcliffe efficiency (NSE) was 0.96 and the Volume Conservation Index (VCI) was 1.03 respectively. The CM-corrected data of Radar and GPM showed good results for the CHABA peak runoff and runoff volume simulation and improved all of $R^2$, NSE, and VCI comparing with the original data application. Thus, we need to use and apply the radar and satellite data to monitor the flood within the watershed.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1193-1201
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• 2020

This study aims to develop a regression model that estimates a low-flow index that can be applied to ungauged basins. A total of 30 midsized basins in South Korea use long-term runoff data provided by the National Integrated Water Management System (NIWMS) to calculate average low-flow, average minimum streamflow, and low-flow index duration and frequency. This information is used in the correlation analysis with 18 basin factors and 3 climate change factors to identify the basin area, average basin altitude, average basin slope, water system density, runoff curve number, annual evapotranspiration, and annual precipitation in the low-flow index regression model. This study evaluates the model's accuracy by using the root-mean-square error (RMSE) and the mean absolute error (MAE) for 10 ungauged, verified basins and compares them with the previous model's low-flow calculations to determine the effectiveness of the newly developed model. Comparative analysis indicates that the new regression model produces average low-flow, attributed to the consideration of varied basin and hydrologic factors during the new model's development.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.969-983
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• 2021

Evapotranspiration, one of the hydrometeorological components, is considered an important variable for water resource planning and management and is primarily used as input data for hydrological models such as water balance models. The FAO56 PM method has been recommended as a standard approach to estimate the reference evapotranspiration with relatively high accuracy. However, the FAO56 PM method is often challenging to apply because it requires considerable hydrometeorological variables. In this perspective, the Hargreaves equation has been widely adopted to estimate the reference evapotranspiration. In this study, a set of parameters of the Hargreaves equation was calibrated with relatively long-term data within a Bayesian framework. Statistical index (CC, RMSE, IoA) is used to validate the model. RMSE for monthly results reduced from 7.94 ~ 24.91 mm/month to 7.94 ~ 24.91 mm/month for the validation period. The results confirmed that the accuracy was significantly improved compared to the existing Hargreaves equation. Further, the evaporative demand drought index (EDDI) based on the evaporative demand (E₀) was proposed. To confirm the effectiveness of the EDDI, this study evaluated the estimated EDDI for the recent drought events from 2014 to 2015 and 2018, along with precipitation and SPI. As a result of the evaluation of the Han-river watershed in 2018, the weekly EDDI increased to more than 2 and it was confirmed that EDDI more effectively detects the onset of drought caused by heatwaves. EDDI can be used as a drought index, particularly for heatwave-driven flash drought monitoring and along with SPI.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.617-626
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• 2022

In Korea, annual precipitation and its variability have gradually increased since modern meteorological observations began, and the risk of disasters has also been increasing due to significant regional variations and recent abnormal climate conditions. Given that damage from storms and floods mainly occurs around rivers, it is crucial to determine the appropriate design frequency for river-related projects. This study examined existing design practices used to determine hydrological design frequencies and suggested a new method to determine appropriate design frequencies. The study collected available data pertaining to seven evaluation factors, specifically the basin areas, shape parameters, channel slopes, stream orders, backwater effect reaches, extreme rainfall frequencies, and urbanized flood inundation areasfor 413 local rivers in Chungcheongnam-do in Korea. The estimated weights for areas of extreme rainfall frequencies and urbanized flood inundation were found to be 18, having a great effect on determining the design frequency. Compared with the established design frequency in previous government reports, the estimated design frequency increased for 255 rivers and decreased for 158 rivers.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.161-170
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• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.