• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.26-67
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• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1251-1260
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• 2019

Existing environmental spatial information, which has been concentrated on spatial resolution, has limitations in solving realistic environmental problems that must be accompanied by physical and chemical characterization. Accordingly, there is a need for an image radar capable of identifying physical characteristics of an object regardless of weather conditions, day and night, and sunlight. Image radar is used in various fields in the United States and Europe. The next generation of medium-sized satellite No. 5 in Korea, which is under development with the aim of monitoring water disasters, is also looking for ways to expand the scope to various applications based on the existing application range. To this end, we analyzed domestic and international papers (100 works) using image radar, and reviewed KEI 2016 report, domestic papers, and foreign papers. Based on this, various environmental issues were summarized and the effects of when the image radar was used were analyzed and land cover was selected as an environmental issue. In the future, we will embody the technology to improve the accuracy of the land cover map, which is the environmental issue selected in this study, and build the foundation system for the stable use of the land cover map.

• Spatial Information Research
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• v.22 no.1
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• pp.75-84
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• 2014

Power lines are one of the main obstacles causing an aircraft crash and thus their realtime detection is significantly important during flight. To avoid such flight obstacles, the use of LIDAR has been recently increasing thanks to its advantages that it is less sensitive to weather conditions and can operate in day and night. In this study, we suggest a fast method to detect power lines from LIDAR data for flight obstacle avoidance. The proposed method first extracts non-ground points by eliminating the points reflected from ground surfaces using a filtering process. Second, we calculate the eigenvalues for the covariance matrix from the coordinates of the generated non-ground points and obtain the ratio of eigenvalues. Based on the ratio of eigenvalues, we can classify the points on a linear structure. Finally, among them, we select the points forming horizontally long straight as power-line points. To verify the algorithm, we used both real and simulated data as the input data. From the experimental results, it is shown that the average detection rate and time are 80% and 0.2 second, respectively. If we would improve the method based on the experiment results from the various flight scenario, it will be effectively utilized for a flight obstacle avoidance system.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.32-40
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• 2008

In this study, we monitor ice cap using calculated NDSI from September to December in 2001, 2003, 2006, 2007 and snow cover area in 2007 decrease by compare with 2001. Global warming is one of the most important issue in this world. Because global-warming is the reason of various meteorological disasters and extreme weather events in these days and snow and glaciers showed that global warming effect most easily. Snow and glaciers play an important role in Earth cooling system because of their high reflectance. The present study has been carried out monitoring ice cap in Himalayas, using MODIS(Moderate Resolution Imaging Spectroradiometer)data. Indicator to monitoring ice cap, NDSI(Normalized Differenced Snow Index) was used in this study. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in visible and short-wave infrared domain to detect snow cover area versus non-snow cover area in a scene. This study is quantitative evaluation about effect of global warming for icecap.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.821-832
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• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.1-18
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• 2022

Currently, the Korea Meteorological Administration evaluates the meteorological drought by region using SPI6(standardized precipitation index 6), which is a 6-month cumulative precipitation standard. However, SPI is an index calculated only in consideration of precipitation at 69 weather stations, and the drought phenomenon that appears for complex reasons cannot be accurately determined. Therefore, the purpose of this study is to calculate and compare SPI considering only precipitation and SDCI (Scaled Drought Condition Index) considering precipitation, vegetation index, and temperature in Gyeonggi. In addition, the advantages and disadvantages of the station data-based drought index and the satellite image-based drought index were identified by using results calculated through the comparison of SPI and SDCI. MODIS(MODerate resolution Imaging Spectroradiometer) satellite image data, ASOS(Automated Synoptic Observing System) data, and kriging were used to calculate SDCI. For the duration of precipitation, SDCI1, SDCI3, and SDCI6 were calculated by applying 1-month, 3-month, and 6-month respectively to the 8 points in 2014. As a result of calculating the SDCI, unlike the SPI, drought patterns began to appear about 2-month ago, and drought by city and county in Gyeonggi was well revealed. Through this, it was found that the combination of satellite image data and station data increased efficiency in the pattern of drought index change, and increased the possibility of drought prediction in wet areas along with existing dry areas.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1119-1130
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• 2021

Urban flooding caused by localized heavy rainfall with unstable climate is constantly occurring, but a system that can predict spatial flood information with weather forecast has not been prepared yet. The worst flood situation in urban area can be occurred with difficulties of structural measures such as river levees, discharge capacity of urban sewage, storage basin of storm water, and pump facilities. However, identifying in advance the spatial flood information can have a decisive effect on minimizing flood damage. Therefore, this study presents a methodology that can predict the urban flood map in real-time by using rainfall data of the Korea Meteorological Administration (KMA), the results of two-dimensional flood analysis and random forest (RF) regression model. The Ujeong district in Ulsan metropolitan city, which the flood is frequently occurred, was selected for the study area. The RF regression model predicted the flood map corresponding to the 50 mm, 80 mm, and 110 mm rainfall events with 6-hours duration. And, the predicted results showed 63%, 80%, and 67% goodness of fit compared to the results of two-dimensional flood analysis model. It is judged that the suggested results of this study can be utilized as basic data for evacuation and response to urban flooding that occurs suddenly.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.