• Spatial Information Research
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• v.18 no.2
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• pp.25-34
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• 2010

This study simulated flood inundation each frequency rainfall using GIS spatial information and FLUMEN model for part of Muju-Namdae Stream. To create geomorphology for the analysis of flood inundation, Triangle Irregular Network(TIN) was constructed using GIS spatial interpolation method based on digital topographic map and river profile data, unique data source to represent real topography of the river areas. And also flood inundation was operated according to the levee collapse to consider extremely flood damage scenarios. As the analysis of result, the inundation area in the left levee collapse showed more high as 3.13, 3.69, and 4.17 times comparing with one of right levee for 50, 100, and 200 year frequency rainfall and showed 1.00, 2.15, and 3.34 times comparing with one of right levee in the inundation depth with over 1.0 meter, which can cause casualties. As the analysis of inundation area of the inundation depth with over 1.0 meter, which can cause casualties in left levee collapse, it increased more high as 263% and 473% when 50 year frequency change into 100 and 200 year frequency. Also As the analysis of inundation area of the inundation depth with over 1.0 meter in right levee collapse, it increased high as 123% and 142% when 50 year frequency change into 100 and 200 year frequency. Especially, the inundation area of the inundation depth with 3.0~3.5m showed more high as 263% and 489% when 50 year frequency change into 100 and 200 year frequency. It is expected that flood inundation map of this paper could be important decision making data to establish land use planning and water treatment measures.

• Journal of agricultural medicine and community health
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• v.36 no.4
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• pp.218-226
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• 2011

Objectives: Paraquat (PQ) is a widely used ionic pesticide that is fatal when ingested accidentally or for suicidal purposes. It is thought that chronic exposure of PQ is related with the development of Parkinson's disease, but epidemiological studies have not yet confirmed that theory. This study attempted to estimate the possibility of environmental PQ exposure through soil and water. Materials and Methods: We analyzed the amount of decomposed PQ solution in wet soil after exposure to ultraviolet light. An artificial rainfall condition was simulated over soil sprayed with PQ to measure the amount of eluted PQ. In addition, PQ was diluted in water from three differently rated rivers and the changes in PQ concentration were measured after ultraviolet exposure over one month. High performance liquid chromatography/ultra violet detection was used to analyze the concentrations of PQ. Results: In the method we used, the recovery rate of PQ showed a precision rate less than 5%, an accuracy greater than 88%, and the calibration equation was y=5538.8x-440.01($R^2$=0.9985). There were no significant differences in the concentrations of PQ obtained from the three specimens over a 1-week period. From the PQ-sprayed soil, the artificial rainfall conditions showed no PQ elution over a 1-month period, and there was no significant differences in PQ concentrations according to ultraviolet exposure among the three samples. Conclusions: PQ remains well adsorbed naturally in soil. However, it may still exist in an integrated state for a long time in the hydrosphere, so the possibility of PQ exposure through drinking water cannot be disqualified.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.106-117
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• 2021

The purpose of this study is prediction of landslide occurrence reflecting the subsurface flow characteristics within the soil layer in the future due to climate change in a large scale watershed. To do this, we considered the infinite slope stability theory to evaluate the landslide occurrence with predicted soil moisture content by SWAT model based on monitored data (rainfall-soil moisture-discharge). The correlation between the SWAT model and the monitoring data was performed using the coefficient of determination (R²) and the model's efficiency index (Nash and Sutcliffe model efficiency; NSE) and, an accuracy analysis of landslide prediction was performed using auROC (area under Receiver Operating Curve) analysis. In results comparing with the calculated discharge-soil moisture content by SWAT model vs. actual observation data, R² was 0.9 and NSE was 0.91 in discharge and, R² was 0.7 and NSE was 0.79 in soil moisture, respectively. As a result of performing infinite slope stability analysis in the area where landslides occurred in the past based on simulated data (SWAT analysis result of 0.7~0.8), AuROC showed 0.98, indicating that the suggested prediction method was resonable. Based on this, as a result of predicting the characteristics of landslide occurrence by 2050 using climate change scenario (RCP 8.5) data, it was calculated that four landslides could occur with a soil moisture content of more than 75% and rainfall over 250 mm/day during simulation. Although this study needs to be evaluated in various regions because of a case study, it was possible to determine the possibility of prediction through modeling of subsurface flow mechanism, one of the most important attributes in landslide occurrence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.404-411
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• 2019

In this study, the HEC-HMS was applied to determine rainfall-runoff processes for the Gokgyuchun basin. Several sub-basins have large-scale reservoirs for agricultural needs and they store large amounts of initial runoff. Three infiltration methods were implemented to reflect the effect of initial loss by reservoirs: 'SCS-CN'(Scheme I), 'SCS-CN' with simple surface method(Scheme II), and 'Initial and Constant rate'(Scheme III). Modeling processes include incorporating three different methods for loss due to infiltration, Clark's UH model for transformation, exponential recession model for baseflow, and Muskingum model for channel routing. The parameters were calibrated using an optimization technique with trial and error method. Performance measures, such as NSE, RAR, and PBIAS, were adopted to aid in the calibration processes. The model performance for those methods was evaluated at Gangcheong station, which is the outlet of study site. Good accuracy in predicting runoff volume and peak flow, and peak time was obtained using the Scheme II and III, considering the initial loss, whereas Scheme I showed low reliability for storms. Scheme III did not show good matches between observed and simulated values for storms with multi peaks. Conclusively, Scheme II provided better results for both single and multi-peak storms. The results of this study can provide a useful tool for decision makers to determine master plans for regional flood control management.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.93-104
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• 2021

Due to climate change the sustainable water resources management of agricultural reservoirs, the largest number of reservoirs in Korea, has become important. However, the DIROM, rainfall-runoff model for calculating agricultural reservoir inflow, has used regression equation developed in the 1980s. This study has optimized the parameters of the DIROM using the genetic algorithm (GA) based on historical inflow data for some agricultural reservoirs that recently begun to observe inflow data. The result showed that the error between the historical inflow and simulated inflow using the optimal parameters was decreased by about 80% compared with the annual inflow with the existing parameters. The correlation coefficient and root mean square error with the historical inflow increased to 0.64 and decreased to 28.2 × 103 ㎥, respectively. As a result, if the DIROM uses the optimal parameters based on the historical inflow of agricultural reservoirs, it will be possible to calculate the long-term reservoir inflow with high accuracy. This study will contribute to future research using the historical inflow of agricultural reservoirs and improvement of the rainfall-runoff model parameters. Furthermore, the reliable long-term inflow data will support for sustainable reservoir management and agricultural water supply.

• Journal of Korean Society of Disaster and Security
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• v.16 no.1
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• pp.13-22
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• 2023

The importance of preemptive flood preparation is growing as the importance of preparing for climate change increases due to record heavy rains in the Seoul metropolitan area in August 2022. Although it is responding to flood control through reservoirs and sediment sites, the government is preparing excellent spill reduction measures through a preliminary consultation system for Low Impact Development (LID). In this study, the depth of flooding was simulated when LID technologies were applied to the Sillim 2-drain region in Dorimcheon Stream basin, an urban stream, using XP-SWMM, a two-dimensional model. In addition, the analysis and applicability of the effect of reducing rainfall runoff for the largest rainfall in a day were reviewed, and it was judged to be effective as a method of reducing flooding in urban areas. Although there is a limitation in which the reduction effect is overestimated, it is thought that the LID technologies can be a significant countermeasure as a countermeasure for small-scale flooded areas where some flooding occurs after structural flooding measures are established.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.194-210
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• 2023

LiDAR plays a key role in autonomous vehicles, where it is used to detect the environment in place of the driver's eyes, and its role is expanding. In recent years, there has been a growing need to test the performance of LiDARs installed in autonomous vehicles. Many LiDAR performance tests have been conducted in simulated and indoor(lab) environments, but the number of tests in outdoor(field) and real-world road environments has been minimal. In this study, we compared LiDAR performance under the same conditions lab and field to determine the relationship between lab and field tests and to establish the characteristics and roles of each test environment. The experimental results showed that LiDAR detection performance varies depending on the lighting environment (direct sunlight, led) and the detected object. In particular, the effect of decreasing intensity due to increasing distance and rainfall is greater outdoors, suggesting that both lab and field experiments are necessary when testing LiDAR detection performance on objects. The results of this study are expected to be useful for organizations conducting research on the use of LiDAR sensors and facilities for LiDAR sensors.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.97-107
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• 2017

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.221-232
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• 2011

Applicability of three soil erosion models for burnt hillslopes was evaluated. The models were estimated with the data from plots established after tremendous wildfire occurred in the east coastal region. Soil erosion and surface runoff were simulated by the Water Erosion Prediction Project (WEPP) and the Revised Universal Soil Loss Equation (RUSLE) of application mode for disturbed forest areas and the Soil Erosion Model for Mountain Areas (SEMMA) developed for burnt hillslopes. Simulated sediment yield and surface runoff were compared with the measured those. In maximum value of sediment yield, three models was under-predicted and RUSLE and WEPP had difference of over two times. SEMMA showed the best model response coefficient, determination coefficient and the model efficiency. In application of models to the soil erosion according to the elapsed year after wildfire, all models were underestimated in initial stage disturbed by wildfire. Evaluation of models in this burnt hillslopes was shown the tends to under-predict soil erosion for larger measured values. Although a lot of sediment can be generated in small rainfall event as fine-grained soil of the high water repellency was exposed excessively right after wildfire, this under-prediction was shown that those models have a limit to estimate the weighted factors by wildfire.