• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.745-755
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• 2000

The dam operation system of KOWACO for flood control doesn't have capability to account for the downstream hydrologic conditions and any feasible index to decide the pre-release from the forecasted rainfall and inflow. In this study, a dam operation model for flood control was developed to account for the flood flow condition of its downstream to give users the dam release schedules. Application test of EV ROM to Keum River showed that EV ROM is superior to the Rigid ROM and Technical ROM which are currently used by KOWACO. EV ROM developed in this study provides a release schedule accounting for the cumulative lateral flow hydrograph at the downstream control points where the discharge does not depend only on the dam operation. but also on lateral inflow from the tributaries. In order to reduce the peak discharge at the control points, it suggests the preliminary release during the early rising phase of the predicted hydrograph, holding the flood flow inside the dam during a peak phase, and afterward resuming the release. Three case studies of flood control by the operation of Daechung Multipurpose Dam in Geum River Basin show that the EV ROM is superior to the Rigid ROM and Technical ROM. This must be due to its nature to account for the downstream flow condition as well as the inflow and water level of the dam. It was also conceived that further case studies of EV ROM and more accurate rainfall prediction would improve the dam operation for flood control.ontrol.

• Korean Journal of Soil Science and Fertilizer
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• v.35 no.2
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• pp.87-92
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• 2002

Soil erosion is detrimental to sustain soil productivity in north Korea, since agriculture of this country depends largely upon the slope land in mountainous area. Taking any measure for protection from erosion should be based on prediction of soil loss. Estimation of rainfall factor, R, in north Korea for the Universal Soil Loss Equation was attempted. The monthly precipitation data of the twenty six locations provided by the Korean Meteorological Adminstration were used. From the relationship between II_30 and the July-August precipitation concentration percents, the regional adjustment factor was obtained. The rainfall factor was calculated with the monthly precipitation data and the regional adjustment factor. The annual precipitation in north Korea ranged from 606 to 1,520mm, and the July-August precipitation concentration percents were 34.4 to 53.8. The regional adjustment factor ranged from 0.53 to 1.33 showing lower value in the highland and east coastal region than in the mid mountainous inland and west region. The R-factor value estimated from the monthly precipitation and the regional adjustment factor ranged from 107 to 483, which was lower than average value in south Korea.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.88-96
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• 2013

Much attention has been needed in water resource management at the watershed due to drought and flooding issues caused by climate change in recent years. Increase in air temperature and changes in precipitation patterns due to climate change are affecting hydrologic cycles, such as evaporation and soil moisture. Thus, these phenomena result in increased runoff at the watershed. The Soil and Water Assessment Tool (SWAT) model has been used to evaluate rainfall-runoff at the watershed reflecting effects on hydrology of various weather data such as rainfall, temperature, humidity, solar radiation, wind speed. For bias-correction of RCP data, at least 30 year data are needed. However, for most gaging stations, only precipitation data have been recorded and very little stations have recorded other weather data. In addition, the RCP scenario does not provide all weather data for the SWAT model. In this study, two scenarios were made to evaluate whether it would be possible to estimate streamflow using measured precipitation and long-term average values of other weather data required for running the SWAT. With measured long-term weather data (scenario 1) and with long-term average values of weather data except precipitation (scenario 2), the estimate streamflow values were almost the same with NSE value of 0.99. Increase/decrease by ${\pm}2%$, ${\pm}4%$ in temperature and humidity data did not affect streamflow. Thus, the RCP precipitation data for Hongcheon watershed were bias-corrected with measured long-term precipitation data to evaluate effects of climate change on streamflow. The results revealed that estimated streamflow for 2055s was the greatest among data for 2025s, 2055s, and 2085s. However, estimated streamflow for 2085s decreased by 9%. In addition, streamflow for Spring would be expected to increase compared with current data and streamflow for Summer will be decreased with RCP data. The results obtained in this study indicate that the streamflow could be estimated with long-term precipitation data only and effects of climate change could be evaluated using precipitation data as shown in this study.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.425-438
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• 2018

Existing studies that analyze the causes and effects of water circulation use mostly rainfall - runoff models, which requires much effort in model development, calibration and verification. In this study, hydrological sensitivity analysis which can separate quantitatively the impacts by natural factors and anthropogenic factor was applied to the Hwacheon dam upper basin from 1967 to 2017. As a result of using various variable change point detection methods, 1999 was detected as a statistically significant change point. Especially, based on the hydrological sensitivity analysis using 5 Budyko based functions, it was estimated that the average inflow reduction amount by Imnam dam construction was $1.890\;billion\;m^3/year$. This results in this study was slightly larger than the those by existing researchers due to increase of rainfall and decrease of Hwacheon dam inflow. In future, it was suggested that effective water management measures were needed to resolve theses problems. Especially, it can be suggested that the monthly or seasonal analysis should be performed and also the prediction of discharge for future climate change should be considered to establish resonable measures.

• Journal of Sensor Science and Technology
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• v.24 no.5
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• pp.291-298
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• 2015

There are many landslides occurred by typhoons and intense rainfall during the summer seasons in Korea. To predict a landslide triggering it is important to understand mechanisms and potential areas of landslides by the geological approaches. However, recent climate changes make difficult to predict landslide based on only conventional prediction methods. Therefore, the importance of a real-time monitoring of landslide using various sensors is emphasized in recent. Many researchers have studied monitoring techniques of landslides and suggested several monitoring systems which can be applicable to the natural terrain. Most sensors of landslide monitoring measure slope displacement, hydrogeologic properties of soils and rocks, changes of stress in soil and rock fractures, and rainfall amount and intensity. The measured values of each sensor are transmitted to a monitoring server in real-time. The ultimate goal of landslide monitoring is to warn landslide occurrence in advance and to reduce damages induced by landslides. This study introduces the current situation of landslide monitoring techniques in each country.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1205-1214
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• 2021

Precipitation is one of a major causes of landslides by rising of pore water pressure, which leads to fluctuations of soil strength and stress. For this reason, precipitation is the most frequently used to determine the landslide thresholds. However, using only precipitation has limitations in predicting and estimating slope stability quantitatively for reducing false alarm events. On the other hand, Soil Moisture (SM) has been used for calculating slope stability in many studies since it is directly related to pore water pressure than precipitation. Therefore, this study attempted to evaluate the appropriateness of applying soil moisture in determining the landslide threshold. First, the reactivity of soil saturation level to precipitation was identified through time-series analysis. The precipitation threshold was calculated using daily precipitation (P_daily) and the Antecedent Precipitation Index (API), and the hydrological threshold was calculated using daily precipitation and soil saturation level. Using a contingency table, these two thresholds were assessed qualitatively. In results, compared to P_daily only threshold, Goesan showed an improvement of 75% (P_daily + API) and 42% (P_daily + SM) and Changsu showed an improvement of 33% (P_daily + API) and 44% (P_daily + SM), respectively. Both API and SM effectively enhanced the Critical Success Index (CSI) and reduced the False Alarm Rate (FAR). In the future, studies such as calculating rainfall intensity required to cause/trigger landslides through soil saturation level or estimating rainfall resistance according to the soil saturation level are expected to contribute to improving landslide prediction accuracy.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.1-8
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• 2018

Extreme climate phenomena are occurring around the world caused by global climate change. The heavy rains exceeds the previous record of highest rainfall. In particular, as flash floods generate heavy rainfall on the mountains over a relatively a short period of time, the likelihood of landslides increases. Gangwon region is especially suffered by landslide damages, because the most of the part is mountainous, steep, and having shallow soil. Therefore, in this study, is to predict the risk of disasters by applying topographic classification techniques and landslide risk prediction techniques to mountain watersheds. Classify the hazardous area by calculating the topographic position index (TPI) as a topographic classification technique. The SINMAP method, one of the earth rock predictors, was used to predict possible areas of a landslide. Using the SINMAP method, we predicted the area where the mountainous disaster can occur. As a result, the topographic classification technique classified more than 63% of the total watershed into open slope and upper slope. In the SINMAP analysis, about 58% of the total watershed was analyzed as a hazard area. Due to recent developments, measures to reduce mountain disasters are urgently needed. Stability measures should be established for hazard zone.

• Journal of Korean Society of Disaster and Security
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• v.17 no.1
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• pp.1-8
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• 2024

In Korea, mountainous areas cover 60% of the land, leading to increased factors such as concentrated heavy rainfall and typhoons, which can result in debris flow and landslide. Despite the high risk of disasters like landslides and debris flow, there has been a tendency in most regions to focus more on post-damage recovery rather than preventing damage. Therefore, in this study, precise topographic data was constructed by conducting on-site surveys and drone measurements in areas where debris flow actually occurred, to analyze the risk zones for such events. The numerical analysis program RAMMS model was utilized to perform debris flow analysis on the areas prone to debris flow, and the actual distribution of debris flow was compared and analyzed to evaluate the applicability of the model. As a result, the debris flow generation area calculated by the RAMMS model was found to be 18% larger than the actual area, and the travel distance was estimated to be 10% smaller. However, the simulated shape of debris flow generation and the path of movement calculated by the model closely resembled the actual data. In the future, we aim to conduct additional research, including model verification suitable for domestic conditions and the selection of areas for damage prediction through debris flow analysis in unmeasured watersheds.