• Journal of the Korean Geographical Society
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• v.43 no.6
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• pp.1002-1015
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• 2008

We investigate the potential of digital elevation model and multivariate geostatistical kriging in mapping of temperature and rainfall based on sparse weather station observations. By using elevation data which have reasonable correlation with temperature and rainfall, and are exhaustively sampled in the study area, we try to generate spatial distributions of temperature and rainfall which well reflect topographic effects and have less smoothing effects. To illustrate the applicability of this approach, we carried out a case study of Jeju island using observation data acquired in January, April, August, and October, 2005. From the case study results, accounting for elevation via colocated cokriging could reflect detailed topographic characteristics in the study area with less smoothing effects. Colocated cokriging also showed much improved prediction capability, compared to that of traditional univariate ordinary kriging. According to the increase of the magnitude of correlation between temperature or rainfall and elevation, much improved prediction capability could be obtained. The decrease of relative nugget effects also resulted in the improvement of prediction capability.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.369-372
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• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes comprising a sensing part and a communication part are developed to detect ground movement. Sensing part is designed to measure inclination angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15.1) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of experimental studies was performed at a small-scale earth slope equipped with an artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope starts to move. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.627-636
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• 1999

In 60 years when the double $CO_2$concentration is anticipated the average annual rainfall depth is expected to be increased by 5 10% due to global warming. However, in the water resources area the frequency change of meteorological extremes such as droughts and floods attracts more interests than the increase of annual rainfall amount. Even though recent frequent occurrences of this kind of meteorological extremes are assumed as an indirect proof of global warming, the prediction of its overall tendency has not yet been made. Thus, in this research we propose a possible methodology to be used for its prediction. The methodology proposed is based on the frequency distribution of daily rainfall be Todorovie and Woolhiser(1975), and Katz(1977), where the input parameters are modified to consider the change of monthly or annual rainfall depth and, thus, to result in the change of frequency distribution. We adopt two values(10mm, 50mm) as thresholds and investigate the change of occurrence probability due to the change monthly and annual rainfall depth. these changes do not directly indicate the changes of occurrence probability of floods and droughts, but it may still be a very useful information for their prediction. Finally, the changes of occurrence probability were found to be greater when considering the monthly rainfall rather than the annual rainfall, and those in rainy season than those in dry season.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.225-229
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• 2005

이류모형을 이용한 단기예측 레이더 강우자료와 관측 레이더자료의 비교를 통하여 얻어진 예측오차를 분석하였다. 임의 시점까지의 예측오차 장에 나타나는 확률분포 형태와 공간적 상관성을 분석하여 이들 특성을 반영하는 추후의 예측오차 장을 모의할 수 있었다. 모의된 예측오차 장과 합성된 단기예측 강우 장은 이류모형을 이용한 예측에 따른 불확실성 을 추계학적으로 반영한 예측강우를 제공한다.

• Korean Journal of Hydrosciences
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• v.7
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• pp.125-137
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• 1996

The subject of the paper is the selection of the number and location of raingauge stations among existing ones for the computation of mean areal precipitation and for use as input of real-time flow prediction models. The weighted average method developed by National Weather Service was used to compute MAP over the Boone River basin in Iowa with a 40 year daily data set. Two different searching methods were used to find local optimal solutions. An operational rainfall-runoff model was used to determine the optimal location and number of stations for flow prediction.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.857-865
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• 2013

Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to 2010. It becomes clear that the probability of the extreme rainfall is increasing for last 20 years in Seoul area and the model proposed works relatively well for both point prediction and predictive interval approach.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.599-606
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• 2010

It has been reported that many slope failures in unsaturated soils are mainly caused by downward infiltration due to rainfall. The rainfall characteristics could be an important factor, and more predictable slope failures can be achieved by considering more reasonable rainfall conditions. So, a need exists that these trends of infiltration in weathered soils, which is commonly found in Korea, are assessed by considering rainfall conditions based on phenomenological approach. In this paper, numerical analyses of unsaturated soil slope under rainfall conditions are presented based on the soil-water characteristic curve in the laboratory and huff method. Then the performance of unsaturated weathered soil slopes was evaluated under various conditions after applying the effect of overburden pressure on SWCCs and fines contents. The results demonstrated that the rainfall conditions using Huff method can be very effective and the proper application on analysis is very important to enhance the prediction on unsaturated slope stability.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.6
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• pp.3-14
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• 2005

Accurate estimation of the spatial distribution of rainfall is critical to the successful modeling of hydrologic processes. The objective of this study is to evaluate the applicability of spatially distributed rainfall data. Spatially distributed rainfall was calculated using Kriging method and Thiessen method. The application of spatially distributed rainfall was appreciated to the runoff response from the watershed. The results showed that for each method the coefficient of determination for observed hydrograph was $0.92\~0.95$ and root mean square error was $9.78\~10.89$ CMS. Ordinary Kriging method showed more exact results than Simple Kriging, Universal Kriging and Thiessen method, based on comparison of observed and simulated hydrograph. The coefncient of determination for the observed peak flow was 0.9991 and runoff volume was 0.9982. The accuracy of rainfall-runoff prediction depends on the extent of spatial rainfall variability.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.97-107
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• 2011

For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.