• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.4
• /
• pp.545-550
• /
• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon occurring all over the world. As a result, as the hydrological aspect of the urban watershed changes, the increase in impervious area leads to serious domestic flood damage due to increased rainfall. In order to minimize the damage of life and property, domestic flooding prediction system is needed. In this study, we developed a flood nomogram capable of predicting flooding only by rainfall intensity and duration. This study suggests a method to set the internal water immersion alarm criterion by analyzing the characteristics of the flooding damage in the flooded area in the metropolitan area where flooding is highly possible and the risk of flooding is high. In addition, based on the manhole and the pipe, the water level was set as follows under the four conditions. 1) When manhole overflows, 2) when manhole is full, 3) when 70% of the pipe is reached, and 4) when 60% of the pipe is reached. Therefore, it can be used as a criterion and a predictive measure to cope with the pre-preparation before the flooding starts, through the rainfall that causes the flooding and the flooding damage.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.4
• /
• pp.44-55
• /
• 2013

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• Journal of Korean Society of Forest Science
• /
• v.103 no.1
• /
• pp.98-104
• /
• 2014

This study aims to predict the amount of soil loss from Mt. Palgong's small basin, by using influence factors derived from related models, including RUSLE and MUSLE models, and verify the validity of the model through a comparative analysis of the predicted values and measured values, and the results are as follows: The amount of soil loss were greatly affected by LS factor. In comparison with the measured value of the amount of total soil loss, the predicted values by the two models (RUSLE and MUSLE), appeared to be higher than those of the measured soil loss. Predicted values by RUSLE were closer to values of measured soil loss than those of MUSLE. However, coefficient of variation of MUSLE were lower, but two model's coefficient of variation in similar partial patterns in the prediction of soil loss. RUSLE and MUSLE, prediction soil loss models, proved to be appropriate for use in small mountainous basin. To improve accuracy of prediction of soil loss models, more effort should be directed to collect more data on rainfall-runoff interaction and continuous studies to find more detailed influence factors to be used in soil loss model such as RUSLE and MUSLE.

• Journal of the Korea Convergence Society
• /
• v.8 no.10
• /
• pp.1-8
• /
• 2017

Accurate forecasting enables to effectively prepare for future phenomenon. Especially, meteorological phenomenon is closely related with human life, and it can prevent from damage such as human life and property through forecasting of weather and disaster that can occur. To respond quickly and effectively to oil spill accidents, it is important to accurately predict the movement of oil spills and the weather in the surrounding waters. In this paper, we selected four representative machine learning techniques: support vector machine, Gaussian process, multilayer perceptron, and radial basis function network that have shown good performance and predictability in the previous studies related to oil spill detection and prediction in meteorology such as wind, rainfall and ozone. we suggest the applicability of oil spill prediction model based on machine learning.

• Journal of Korea Water Resources Association
• /
• v.42 no.9
• /
• pp.737-745
• /
• 2009

Field measured sediment yield from an experimental urbanized basin was compared with the predicted sediment yields with RUSLE (Revised Universal Soil Loss Equation), and MUSLE (Modified Universal Soil Loss Equation). The experimental basin is 3.1km2 in area and fifty six percent of the total area had been urbanized. The hydrological data have been measured with T/M at the outlet of the experimental basin. Runoff from the basin and rainfall depth of the basin were measured every minute. Bed load and suspended load were also measured for a given flow rate. Runoff rating curves and sediment rating curve were developed for the last three years. RUSLE showed scattered prediction results but the average of the prediction values was close to the measured one. Meanwhile, MUSLE showed linear correlation between the measured sediment yield and predicted one with high correlation coefficient. But MUSLE predicts high values than the real one. Therefore, adjustment is necessary to apply MUSLE in estimation of sediment yield from the experimental urbanized basin.

• Journal of Korean Society on Water Environment
• /
• v.36 no.4
• /
• pp.300-313
• /
• 2020

The Bayesian approach can be used to estimate hydrologic model parameters from the prior expert knowledge about the parameter values and the observed data. The purpose of this study was to compare the performance of the two Bayesian methods, the Metropolis-Hastings (MH) algorithm and the Generalized Likelihood Uncertainty Estimation (GLUE) method. These two methods were applied to the TANK model, a hydrological model comprising 13 parameters, to examine the uncertainty of the parameters of the model. The TANK model comprises a combination of multiple reservoir-type virtual vessels with orifice-type outlets and implements a common major hydrological process using the runoff calculations that convert the rainfall to the flow. As a result of the application to the Nam River A watershed, the two Bayesian methods yielded similar flow simulation results even though the parameter estimates obtained by the two methods were of somewhat different values. Both methods ensure the model's prediction accuracy even when the observed flow data available for parameter estimation is limited. However, the prediction accuracy of the model using the MH algorithm yielded slightly better results than that of the GLUE method. The flow duration curve calculated using the limited observed flow data showed that the marginal reliability is secured from the perspective of practical application.

• Economic and Environmental Geology
• /
• v.36 no.4
• /
• pp.313-320
• /
• 2003

Slope disaster is one of the repeated occurring geological disasters in rainy season resulting in about 23 human losses in Korea every year. The slope disaster, however, mainly depends on the spatial and climate properties. such as geology, geomorphology, and heavy rainfall, and, hence, the prediction or hazard analysis of the slope disaster is a difficult task. Therefore, GIS and various statistical methods are implemented for slope disaster analysis. In particular, GIS technique is widely used for the analysis because it effectively handles large amount of spatial data. The GIS technique. however, only considers the statistics between slope disaster occurrence and related factors, not the mechanism. Accordingly. an infinite slope model that mechanically considers the balance of forces applied to the slope is suggested here with GIS for slope disaster analysis. According to the research results, the infinite slope model has a possibility that can be utilized for landslide prediction and hazard evaluation since 87.5％ of landslide occurrence areas have been predicted by this technique.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.9 no.5
• /
• pp.93-102
• /
• 2009

A prediction of streamflow based on multi-objective function is presented to check the performance of Probability Distributed Model(PDM) in Miho stream basin, Chungcheongbuk-do, Korea. PDM is a lumped conceptual rainfall runoff model which has been widely used for flood prevention activities in UK Environmental Agency. The Monte Carlo Analysis Toolkit(MCAT) is a numerical analysis tools based on population sampling, which allows evaluation of performance, identifiability, regional sensitivity and etc. PDM is calibrated for five model parameters by using MCAT. The results show that the performance of model parameters(cmax and k(q)) indicates high identifiability and the others obtain equifinality. In addition, the multi-objective function is applied to PDM for seeking suitable model parameters. The solution of the multi-objective function consists of the Pareto solution accounting to various trade-offs between the different objective functions considering properties of hydrograph. The result indicated the performance of model and simulated hydrograph are acceptable in terms on Nash Sutcliffe Effciency*(=0.035), FSB(=0.161), and FDBH(=0.809) to calibration periods, validation periods as well.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.305-305
• /
• 2019

태풍에 의한 재해는 우리나라에서 발생하는 자연재해 중 발생빈도가 가장 높은 것으로 나타나며, 최근 들어 태풍 및 집중호우로 인한 홍수가 급증하고 있는 실정이다. 최근에는 치수증대사업으로 하천 범람의 재해가 감소하는 추세이지만, 도시지역의 경우 도시개발에 따른 내수 범람 피해가 증가하고 있고, 산지에서는 토석류 등의 토사 재해가 증가하고 있다. 이러한 홍수피해를 경감하기 위해서는 치수사업 등과 같은 구조적인 대책도 필요하지만, 정확한 홍수 예 경보를 통한 대비시간의 확보 등과 같은 비구조적인 대책도 중요하며, 홍수 예 경보를 통한 선행시간(Lead time)확보를 위해 강우 및 홍수예측 시스템 구축이 하나의 대안으로 대두되고 있다. 강우예측 기법으로는 레이더(Radar)를 통해 관측된 자료를 외삽하는 초단기 강우예측기법이 최근까지 많이 수행되어 왔다. 하지만 컴퓨터 계산 능력이 향상되면서 수치예보(Numerical Weather Prediction; NWP) 모델을 이용한 강우예측 및 수문학적 적용에 관한 연구들이 대두되고 있다. 본 연구에서는 수치예보모델을 이용하여 기상 및 수자원 간의 연계를 통한 강우 및 홍수 예측에 활용방안을 검토하기 위해 한국 기상청에서 제공하는 국지예보모델(LDAPS)과 예측 도메인에 한국을 포함하는 일본 기상청의 중규모 모델(MSM)을 이용하여 남강댐 유역 내 산청 유역에 대해 강우 및 홍수 예측 정확도를 평가하고 비교 검토하였다. 본 연구에서 적용한 LDAPS와 MSM은 사용하는 수치모델, 물리과정 매개변수, 자료동화 기법 및 지배 방정식 등이 다르기 때문에 직접적인 비교를 하는데 무리가 있지만 국내의 강우 및 홍수 예측 분야에서의 각 수치예보모델의 활용성을 검토하고자 한다.

• Ecology and Resilient Infrastructure
• /
• v.7 no.4
• /
• pp.345-352
• /
• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.