• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Ginseng Culture
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• v.4
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• pp.128-141
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• 2022

Baengnyeongdo Island, which belongs to Ongjin-gun, Incheon, is an island in the northernmost part of the West Sea in South Korea. Baengnyeong Island is the 15th largest island in Korea and covers an area of 51 km². The Korea Ginseng Corporation (KGC) investigated the possibility of growing ginseng on Baengnyeong Island in 1996. In 1997, thanks to the support of cultivation costs from Ongjin-gun, the first ginseng seedbed was built on Baengnyeong Island. In 1999, the seedlings were transplanted to a permanent field under a contract with KGC. In 2003, the first six-year-old ginseng harvest was performed, and KGC purchased all production according to the contract. Since then, KGC has signed on to grow ginseng until 2012 and purchased six-year-old ginseng until the fall of 2016. Since 2014, the GimpoPaju Ginseng Agricultural Cooperative Association has signed a ginseng production contract. According to a survey of nine 6-year-old ginseng fields (total 5,961 units) on Baengnyeong Island, the top five with good growth had a survival rate of 42.6 to 68%, and the bottom four with poor growth had an extremely low survival rate of 11.1 to 21.3%. The four fields with low survival rates were where hot peppers were planted before ginseng cultivation. It is believed that the excess nitrogen remaining in the soil due to the treatment of compost or manure during pepper cultivation causes ginseng roots to rot. The average incidence of Alternaria blight was 8.6%. Six six-year-old ginseng gardens were low at 1.1 to 4.7%, while the other three were high at 16.7 to 20.9%. It is assumed that the reason for the low survival rate and high incidence of Alternaria blight is a rain-leaking shield. Farmers used rain-leaking shields because the precipitation on Baengnyeong Island was smaller than on land. One field showed 3% of leaves with yellowish brown spots, a symptom of physiological disturbance of the leaf, which is presumed to be due to the excessive presence of iron in the soil. To increase the production of ginseng on Baengnyeong Island, it is necessary to develop a suitable ginseng cultivation method for the island, such as strengthening the field management based on the results of a scientific study of soil, using rain-resistant shading, and installing drip irrigation facilities. I hope that ginseng will become a new driving force for the development of Baengnyeong Island, allowing ginseng products and food to thrive in the beautiful natural environment of the island.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.94-106
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• 2022

In order to increase timber self-sufficiency, Korea's 6th Basic Forest Plan aims to increase the density of forest roads to 12.8 m ha^-1 by 2037. However, due to rapid re-forestation, current management infrastructure is insufficient, with just 4.8 m ha^-1 of forest roads in 2017. This is partly due to time and cost limitations on the process of forest road feasibility evaluation, which considers factors such as topography and forest conditions. To solve this problem, we propose an eco-friendly and efficient forest road network planning method using a geographic information system (GIS), which can evaluate a potential road site remotely based on spatial information. To facilitate such planning, this study identifies forest road construction priorities that can be evaluated using spatial information, such as topography, forest type and forest disasters. A method of predicting the optimal route to connect a forest road with existing roads is also derived. Overlapping analysis was performed using GIS-MCE (which combines GIS with multi-criteria evaluation), targeting the areas of Cheongsong-gun and Buk-gu, Pohang-si, which have a low forest-road density. Each factor affecting the suitability of a proposed new forest road site was assigned a cost, creating a cost surface that facilitates prioritization for each forest type. The forest path's optimal route was then derived using least-cost path analysis. The results of this process were 30 forestry site recommendations in Cheongsong-gun and one in Buk-gu, Pohang-si; this would increase forest road density for the managed forest sites in Cheongsong-gun from 1.58 m ha^-1 to 2.55 m ha^-1. This evaluation method can contribute to the policy of increasing timber self-sufficiency by providing clear guidelines for selecting forest road construction sites and predicting optimal connections to the existing road network.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.902-909
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• 2023

This study evaluated the performance test of a small-sized LTE-Maritime(LTE-M) transceiver that was developed and promoted to expand the use of intelligent maritime traf ic information services led by the Ministry of Oceans and Fisheries with the aim of supporting the prevention of maritime accidents. Accoriding to statistics, approximately 30% of all marine accidents in Korean water occur with ships weighing less than 3 tons. Therefore, the blind spots of maritime safety must be supplemented through the development of small-sized transceivers. The small transceiver may be used in fishing boats that are active near coastal waters and in water leisure equipment near the coastline. Therefore, verifying whether sufficient performance and stable communication quality are provided is necessary, considering the environment of their real usage. In this study, we reviewed the communication quality goals of the LTE-M network and the performance requirements of small-sized transceivers suggested by the Ministry of Oceans and Fisheries, and proposed a test plan to appropriately evaluate the performance of small-sized transceivers. The validity of the proposed test method was verified for six real-sea areas with a high frequency of marine accidents. Consequently, the downlink and uplink transmission speeds of the small-sized LTE-M transceiver showed performances of 9 Mbps or more and 3 Mbps or more, respectively. In addition, using the coverage analysis system, coverage of more than 95% and 100% were confirmed in the intensive management zone (0-30 km) and interesting zone (30-50 km), respectively. The performance evaluation method and test results proposed in this paper are expected to be used as reference materials for verifying the performance of transceivers, contributing to the spread of government-promoted e-navigation services and small-sized transceivers.

• Korean Journal of Ecology and Environment
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• v.57 no.1
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• pp.39-50
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• 2024

In ecosystems within limited resources, interspecific competition is inevitable, often leading to the competitive exclusion of inferior species. This study aims to provide foundational information for the conservation and restoration management of Microphysogobio rapidus by evaluating species' ecological response to biological factors within its habitat. To understand this relationship, we collected food web organisms from site where M. rapidus coexist with Microphysogobio yaluensis, a specie ecologically similar to M. rapidus, and evaluated the trophic levels (TL), isotopic niche space (INS), and the overlap of INS among fishes within the habitat using stable isotope analysis. Our analysis revealed that the M. rapidus exhibited a higher TL than M. yaluensis, with TL of 2.6 and 2.4, respectively. M. yaluensis exhibited a broad INS, significantly influencing the feeding characteristics of most fish. Conversely, M. rapidus showed a narrow INS and asymmetric feeding relationships with other species, in habitats with high competition levels. This feeding characteristics of M. rapidus indicate that the increase in competitors sharing the similar resources lead to a decrease in available resources and, consequently, is expected to result in a decrease in their density.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.