• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• 한국농공학회논문집
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• 제61권2호
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• pp.63-74
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• 2019

The objective of this study was to evaluate the influence of rainfall observation network on daily dam inflow using artificial neural networks(ANNs). Chungju Dam and Soyangriver Dam were selected for the study watershed. Rainfall and dam inflow data were collected as input data for construction of ANNs models. Five ANNs models, represented by Model 1 (In watershed, point rainfall), Model 2 (All in the Thiessen network, point rainfall), Model 3 (Out of watershed in the Thiessen network, point rainfall), Model 1-T (In watershed, area mean rainfall), Model 2-T (All in the Thiessen network, area mean rainfall), were adopted to evaluate the influence of rainfall observation network. As a result of the study, the models that used all station in the Thiessen network performed better than the models that used station only in the watershed or out of the watershed. The models that used point rainfall data performed better than the models that used area mean rainfall. Model 2 achieved the highest level of performance. The model performance for the ANNs model 2 in Chungju dam resulted in the $R^2$ value of 0.94, NSE of 0.94 $NSE_{ln}$ of 0.88 and PBIAS of -0.04 respectively. The model-2 predictions of Soyangriver Dam with the $R^2$ and NSE values greater than 0.94 were reasonably well agreed with the observations. The results of this study are expected to be used as a reference for rainfall data utilization in forecasting dam inflow using artificial neural networks.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• 한국지구과학회지
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• 제31권5호
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• pp.465-474
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• 2010

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

• 한국물환경학회지
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• 제32권3호
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• pp.310-317
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• 2016

It has been a big problem to estimate rainfall for the studies of mud-debris flows because the estimated rainfall from the nearest AWS (Automatic Weather Station) can tend to be quite inaccurate at individual sites. This study attempts to improve this problem through accurate rainfall depth estimation by applying an artificial neural network with radar rainfall data. For this, three models were made according to utilizing methodologies of rainfall data. The first model uses the nearest rainfall, observing the site from an ungauged site. The second uses only radar rainfall data and the third model integrates the above two models using both radar and observed rainfall at the sites around the ungauged site. This methodology was applied to the metropolitan area in Korea. It appeared as though the third model improved rainfall estimations by the largest margin. Therefore, the proposed methodology can be applied to forecast mud-debris flows in ungageed sites.

• 한국산학기술학회논문지
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• 제20권12호
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• pp.828-835
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• 2019

인공강우장치는 실내실험 기반의 모형실험 데이터를 생성하는 연구도구로 널리 이용되고 있다. 다양한 연구에 인공강우장치가 이용되고 있음에도 불구하고 대부분의 연구에서 강우분포의 균질성에 대한 논의는 등한시되고 있다. 다양한 강우특성이 반영된 급경사지 붕괴 모의실험을 위해서는 저강도에서 고강도까지 강우를 모사할 수 있는 강우장치가 필수적이며 실험의 신뢰성을 확보하기 위해서라도 강우분포의 균질성은 확보되어야 한다. 본 연구에서는 급경사지 붕괴모의실험의 주요설비인 인공강우장치의 최대 강우강도 130mm/hr 내에서 10mm/hr 단위 제어를 목표로 하며, 균등계수 80% 이상 확보하고자 하였다. 이를 위해 노즐타입, 크기, 위치 및 펌프압력에 따른 다양한 조건하에서 강우실험을 수행하였다. 실험결과 노즐형태는 원형노즐, 크기는 1.9mm와 1.4mm가 적합한 것으로 분석되었고 적정 펌프압력은 3~6kg/㎠으로 분석되었다. 다양한 강우강도를 재현하기 위해 노즐수는 2, 3, 5개, 펌프압력은 3, 4, 5, 6kg/cm2로 조건을 달리하여 실험을 수행하였다. 펌프압력이 증가함에 따라 강우강도는 선형적으로 증가하는 경향을 보였다. 실험결과를 바탕으로 펌프압력, 노즐형태 및 노즐수를 변수로 하는 강우제어 매뉴얼을 작성하였다. 또한 검증실험을 수행하여 목표 강우강도 대비 오차범위 ±3.1%, 균등계수는 평균 86.8%로 균질한 강우분포를 보였다.

• 한국농공학회지
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• 제45권2호
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2001년도 봄 학술발표회 논문집
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• pp.199-206
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• 2001

Landslides generally occur due to influences of the internal and external factors. Internal factors include ground characteristics, terrain and so on. External factors can also be divided into natural factors such as rainfall, ground water, earthquake and so on, and artificial factors resulting from cutting and embankments. Among these factors, rainfall becomes the most important external factors by means of which landslides occur in Korea. To appropriately deal with tile effects of pore water pressures due to rainfall, the method using the pore water pressure ratio(r$\_$u/) is generally applied in slope stability analysis or the design of slope reinforcements. Since tire value of r,, is in general not constant over the whole cross section, in most slope stability analyses the average values are used with little loss in accuracy. However, determination of the average values of r$\_$u/ to applied in the design is difficult problem. Therefore, in this study, tile average values of r$\_$u/ according to the intensity of rainfall and slope inclination is suggested based on results of the small scaled model tests using the artificial rainfall apparatus. It is found from the model tests that the average values of r$\_$u/ is about 0.07∼0.18(in case of the intensity of rainfall is 50mm/hr.), about 0.10∼0.28(in case of the intensity of rainfall is 100mm/hr.), and about 0.10∼0.33(in case of the intensity of rainfall is 150mm/hr.).

• 대한토목학회논문집
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• 제38권2호
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• pp.227-235
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• 2018

본 연구에서는 차량용 레인센서를 이용하여 고해상도의 강우관측을 위한 관계식을 개발하였다. 차량용 레인센서는 8개의 채널로 이루어져 있으며, 각 채널은 강우발생시 차량의 전면유리창에 내리는 우적량을 감지하여 센서시그널을 생성하는데, 강우량이 높을수록 센서시그널은 낮은 값으로 형성된다. 레인센서에서 생성되는 센서시그널의 이러한 특징을 이용하여 관계식을 개발하였다. 특정강우를 발생시키기 위하여 인공강우 발생장치를 제작하였으며, 인공강우발생장치에서 분사되는 강우량의 변화에 따른 센서시그널의 변화 값을 분석하였다. 이 중 민감도 분석을 통해 다양한 강우량을 잘 반영하는 최적의 센서 채널을 선정하였다. 선정된 채널을 이용하여 5분 단위 센서시그널를 생성하였고 생성된 5분 단위 센서시그널의 대표 값을 평균, 25분위, 50분위, 75분위로 설정하여 관계식을 구축하였다. 구축된 관계식을 이용하여 실강우 데이터에 적용하여 강우량 값을 환산하였고, 환산된 강우량은 지상강우관측소에서 관측된 강우량 값과의 비교를 통해 관계식의 신뢰도를 검증하였다. 검증결과 데이터의 이상치가 발견되어 관계식의 신뢰도는 다소 떨어졌지만, 해당 잔차 범위의 실험 데이터가 부족한 것으로 판단되었다. 개발된 관계식을 실강우에 적용시켜 강우량 값을 환산한 하였고, 신뢰도 검증을 위해 동시간에 관측된 지상강우 관측 장비에서 생성된 강우량 값과 비교를 하였고 관측 결과 레인센서는 0.5mm 이하의 미세한 강우량까지 측정하였고 평균 관측 오차는 0.36mm로 나타났다.

• Geomechanics and Engineering
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• 제22권2호
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• pp.153-163
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• 2020

Laboratory experiments were conducted with two different soil conditions to investigate rainfall infiltration characteristics. The soil layer materials that were tested were weathered granite soil and weathered gneiss soil. Artificial rainfall of 80 mm/hr was reproduced through the use of a rainfall device, and the volumetric water content and matric suction were measured. In the case of the granite soil, the saturation velocity and the moving direction of the wetting front were fast and upward, respectively, whereas in the case of the weathered gneiss soil, the velocity and direction were slow and downward, respectively. Rainfall penetrated and saturated from the bottom to the top as the hydraulic conductivity of the granite soil was higher than the infiltration capacity of the artificial rainfall. In contrast, as the hydraulic conductivity of the gneiss soil was lower than the infiltration capacity of the rainfall, ponding occurred on the surface: part of the rainfall first infiltrated, with the remaining rainfall subsequently flowing out. The unsaturated hydraulic conductivity function of weathered soils was determined and analyzed with matric suction and the effective degree of saturation.