• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.248-248
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• 2023

In recent years, the number of landslides in Korea has been increasing due to extreme weather events such as localized heavy rainfall and typhoons. Landslides often occur with debris flows, land subsidence, and earthquakes. They cause significant damage to life and property. 64% of Korea's land area is made up of mountains, the government wanted to predict landslides to reduce damage. In response, the Korea Forest Service has established a 'Landslide Information System' to predict the likelihood of landslides. This system selects a total of 13 landslide factors based on past landslide events. Using the LR technique (Logistic Regression) to predict the possibility of a landslide occurrence and the accuracy is known to be 0.75. However, most of the data used for learning in the current system is on landslides that occurred from 2005 to 2011, and it does not reflect recent typhoons or heavy rain. Therefore, in this study, we will apply a total of six machine learning techniques (KNN, LR, SVM, XGB, RF, GNB) to predict the occurrence of landslides based on the data of Inje, Gangwon-do, which was recently produced by the National Institute of Forest. To predict the occurrence of landslides, it is necessary to process converting landslide events and factors data into a suitable form for machine learning techniques through ArcGIS and Python. In addition, there is a large difference in the number of data between areas where landslides occurred or not. Therefore, the prediction was performed after correcting the unbalanced data using Tomek Links and Near Miss techniques. Moreover, to control unbalanced data, a model that reflects soil properties will use to remove absolute safe areas.

• Journal of the Korean Society of Safety
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• v.32 no.1
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• pp.53-59
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• 2017

Safety inspection activities of the facilities including the electricity, gas, building, and firefighting, etc. are individual checks by the separate law of each government department, comprehensive inspections for the specific managed facilities or during a weak season, and national safety overall diagnostics for the disaster prevention. Thus, types of the inspections are various and they have been carried out repeatedly as well as duplicately. That can make the people or institutes to take such inspections feel great burdens. Therefore, the investigation on the current situation of the individual inspection by the separate law for the electricity, gas, building, and firefighting, etc. as well as of various others by the government needs to be carried out and according to the results the repetitive and duplicated inspections should be reduced and converged to one comprehensive one. In this regard, we proposed solutions to improve the government safety inspection system, function, and role.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.101-112
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• 2018

A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.599-603
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• 2023

Analyzed in the NFDS, The average number of fires in vinyl temporary buildings is more than 1,000 in annually. And the number of deaths and injuries was around 30 people in every year. According to the National Fire Agency, There are 142,386 vinyl temporary buildings for Agriculture and Fishery, of which 4,720 are residential vinyl temporary buildings in illegally. Model house is subject to regulation in fire-related law. But, even though it's the same temporary building, Vinyl temporary building is not subject to regulation. For this reason, Vinyl temporary buildings are left in the blind spot of fire safety. Therefore, In this study, We propose that amend of Act on installation and management of firefighting systems, make of temporary fire safety controller, develop and apply of alarm system that is connected to a single-alarm smoke dretector, organize and operate volunteer fire brigade of the crop group to prevent and extinguish the fire.

• Journal of the Society of Disaster Information
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• v.8 no.4
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• pp.419-431
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• 2012

This paper takes precautions proposals against prospective disasters from the space weather maximum in 2013. A geomagnetic space storm sparked by a solar maximum like the one that flared toward earth is bound to strike again and could wreak havoc across the modern world. The purpose of the study is that the disaster reduction and safety service implementation study on the ultimate space weather systems by the information systems of the space weather. The process methods of the study are that an implementation of preparation for the smart IT and GIS based disaster management systems of the solar maximum deal with analysis on the flare, solar proton event, and geomagnetic storm from space blasters, These approach and methods for the solar maximin display national policy implementation of the pattern of the radio wave disasters from the protection and preparation methods. This research can provide affective methods for the saving lives and property protections that implementation of the disaster prediction and disaster prevention systems adapts the smart IT systems and converged decision making support systems using uGIS methodology.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.1
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• pp.57-63
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• 2015

The incidence of fires caused by electrical factors has increased with the growth in domestic electrical consumption. According to the national fire data system of national emergency management agency, electrical fires accounted for 20% of all domestic fires in the last 10 years. Electrical fires are mainly caused by short circuit, leakage current, defect in an electrical equipment, over load, utility fault, etc. The fault current can be several times larger than the nominal current, thereby exceeding the rated current of cable. Consequently, the cable conductor, typically copper wire, heats up to a temperature that ignites surrounding combustibles. This paper describes the transient characteristics of the 0.6/1kV, TFR-8 cable have been investigated, and analyzed under the over current conditions for reduce the risk of electrical fire by experimental and FEM analysis. The experimental and FEM(Finite Element Method) analysis results of temperature and resistance variation according to the over current in copper wires were analyzed. The experimental results coincide well with the FEM analysis.

• Journal of the Korean GEO-environmental Society
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• v.18 no.1
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• pp.37-47
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• 2017

The Mekong River plays an extremely important role in Southeast Asia. Flowing through six countries, including China, Myanmar, Thailand, Laos PDR, Cambodia, and Vietnam, it is a site of great biological and ecological diversity and the habitat of numerous species of fish. It also supports a very large population that lives along the river basin. Therefore, much attention has been focused on the giant Mekong River Basin, particularly, its soil erosion and sedimentation problems. In fact, many methods have been used to calculate and simulate these problems. However, in the case of the Mekong River Basin, the available data is limited because of the extreme size of the area (about $795,000km^2$) and lack of equipment systems in the countries through which the Mekong River flows. In this study, we applied the Universal Soil Loss Equation (USLE) model in a GIS (Geographic Information System) framework to calculate the amount of soil erosion and sediment load during the selected period, from 1951 to 2007. The result points out dangerous areas, such as the Upper Mekong River Basin and 3S Basin (containing the Sekong, Sesan, and Srepok Rivers) that are suffering the serious consequences of soil erosion problems. Moreover, the present model is also useful for supporting river basin management in the implementation of sustainable management practices in the Mekong River Basin and other basins.

• Journal of Environmental Science International
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• v.18 no.4
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• pp.355-367
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• 2009

Comparison study on the land surface temperatures, which are calculated from four different algorithms for MODIS data, was carried out and the characteristics of each algorithm on land surface temperature estimation were also analysed in this study. Algorithms, which are well used for various satellite data analysis, in the comparisons are proposed by Price, Becker and Li, Ulivieri et al., and Wan. Verification of estimated land surface temperature from each algorithm is also performed using observation based regression data. The coefficient of determination ($R^2$) for daytime land surface temperature estimated from Wan's algorithm is higher than that of another algorithms at all seasons and the value of $R^2$ reach on 0.92 at spring. Although $R^2$ for Ulivieri's algorithm is slightly lower than that for Wan's algorithm, the variation pattern of land surface temperature for two algorithms are similar. However, the difference of estimated values among four algorithms become small at the region of high land surface temperature.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.