• Atmosphere
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• v.21 no.1
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• pp.1-16
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• 2011

Numerical prediction capability has been improved over the decades, but progress of prediction for high-impact weather (HIW) was unsatisfactory. One reason of low predictability for HIW is lack of observation data. The National Institute of Meteorological Research (NIMR) has been performed observation program for improvement of predictability, and reduction in social and economical cost for HIW. As part of this observation program, summer intensive observation program (ProbeX-2009) was performed at the observation-gap areas from 25 August to 6 September 2009. Sounding observations using radiosonde were conducted in the Gisang2000 research vessel (R/V) from the Korea Meteorological Administration (KMA) over the West Sea and the Eardo R/V from the Korea Ocean Research and Development Institute (KORDI) over the South Sea. Observation System Experiment (OSE) is carried out to examine the effect of ProbeX-2009 data. OSEs using Korea Local Analysis and Prediction System (KLAPS) and Weather Research and Forecasting (WRF) Model are conducted to investigate the predictability for a short time forecast. And, OSEs using WRF/3DVAR system and WRF forecast model are conducted to study the predictability for an extended time. Control experiment (K_CTL and CNTL) used only GTS observation and experiment (K_EXP and SWEXP) used ProbeX-2009 data from two system are performed. ETS for 3hr accumulated rainfall simulated by KLAPS-WRF shows that K_EXP is higher than K_CTL. Also, ETS for 12hr accumulated rainfall of SWEXP from 3DVAR-WRF is higher than CNTL. The results indicate that observation over the ocean has positive impact on HIW prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Atmosphere
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• v.27 no.4
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• pp.385-398
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• 2017

Numerical weather prediction experiments were carried out by applying topographic effects to reduce or enhance the solar radiation by terrain. In this study, x and ${\kappa}({\phi}_o,\;{\theta}_o)$ are precalculated for topographic effect on high resolution numerical weather prediction (NWP) with 1 km spatial resolution, and meteorological variables are analyzed through the numerical experiments. For the numerical simulations, cases were selected in winter (CASE 1) and summer (CASE 2). In the CASE 2, topographic effect was observed on the southward surface to enhance the solar energy reaching the surface, and enhance surface temperature and temperature at 2 m. Especially, the surface temperature is changed sensitively due to the change of the solar energy on the surface, but the change of the precipitation is difficult to match of topographic effect. As a result of the verification using Korea Meteorological Administration (KMA) Automated Weather System (AWS) data on Seoul metropolitan area, the topographic effect is very weak in the winter case. In the CASE 1, the improvement of accuracy was numerically confirmed by decreasing the bias and RMSE (Root mean square error) of temperature at 2 m, wind speed at 10 m and relative humidity. However, the accuracy of rainfall prediction (Threat score (TS), BIAS, equitable threat score (ETS)) with topographic effect is decreased compared to without topographic effect. It is analyzed that the topographic effect improves the solar radiation on surface and affect the enhancements of surface temperature, 2 meter temperature, wind speed, and PBL height.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.198-203
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• 2020

In this paper, we propose an artificial water level prediction system for small river flood prediction. River level prediction can be a measure to reduce flood damage. However, it is difficult to build a flood model in river because of the inherent nature of the river or rainfall that affects river flooding. In general, the downstream water level is affected by the water level at adjacent upstream. Therefore, in this study, we constructed an artificial intelligence model using Recurrent Neural Network(LSTM) that predicts the water level of downstream with the water level of two upstream points. The proposed artificial intelligence system designed a water level meter and built a server using Nodejs. The proposed neural network hardware system can predict the water level every 6 hours in the real river.

• Water for future
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• v.25 no.3
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• pp.105-113
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• 1992

The purpose of this paper is to introduce and to apply the artificial neural network theory to real hydrologic system for forecasting daily streamflows during flood periods. The hydrologic dynamic process of rainfall-runoff is identified by the iterated estimation of system parameters that are determined by adjusting the weights of the network according to the non-linear response characteristics which is formed the model. Back propagation algorithm of neural network model is applied for the estimation of system parameters with past daily rainfall and runoff series data, and streamflows are forecasted using the parameters. The forecasted results are analyzed by statistical methods for the comparison with the observed.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.1
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• pp.27-38
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• 2012

This study is to analyse the hydrological behavior of agricultural reservoir using CAT (Catchment hydrologic cycle Assessment Tool). The CAT is a water cycle analysis model in order to quantitatively assess the characteristics of the short/long-term changes in watershed. It supports the effective design of water cycle improvement facilities by supplementing the strengths and weaknesses of existing conceptual parameter-based lumped hydrologic models and physical parameter-based distributed hydrologic models. The CAT especially supports the analysis of runoff processes in paddy fields and reservoirs. To evaluate the impact of agricultural reservoir operation and irrigation water supply on long-term rainfall-runoff process, the CAT was applied to Idong experimental catchment, operated for research on the rural catchment characteristics and accumulated long term data by hydrological observation equipments since 2000. From the results of the main control points, Idong, Yongdeok and Misan reservoirs, the daily water levels of those points are consistent well with observed water levels, and the Nash-Sutcliffe model efficiencies were 0.32~0.89 (2001~2007) and correlation coefficients were 0.73~0.98.

• Environmental Engineering Research
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• v.25 no.4
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• pp.545-553
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• 2020

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.611-617
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• 2008

It is necessary to analysis the data characteristics of discharge and water quality for efficient water resources management, aggressive alternatives to inundation by flood and various water pollution accidents, the basic information to manage water quality in lakes and to make environmental policy. Therefore, the present study applied Self-Organizing Map (SOM) showing excellent performance in classifying patterns with weights estimated by self-organization. The result revealed five patterns and TOC versus rainfall-storage data according to the respective patterns were depicted in two-dimensional plots. The visualization presented better understanding of data distribution pattern. The result in the present study might be expected to contribute to the modeling procedure for data prediction in the future.

• Journal of Biosystems Engineering
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• v.25 no.3
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• pp.195-202
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• 2000

This study was conducted to recognize a possibility that cone index can be used as a means of evaluating the tillage workability. Cone indexes were measured every 24 hours after rainfall at the experimental plots, and the rotary and plowing operations were conducted at the same time. The workability was evaluated on a basis of three categories of good, fair and poor depending on the quality of the performed works. Although the workability was affected by many factors such as soil type, moisture content ground slope and weather condition, the duration and amount of rainfall were of most influence. Results of the study showed that a good workability was resulted from the cone indexes greater than an average of 552 kPa for rotary operations and 671 kPa for plowing operations. Fair work was obtained with cone indexes greater than an average of 331 kPa for rotary operations and 459 kPa for plowing operations. The cone indexes less than an average of 171 kPa and 149 kPa resulted in poor workabilities for rotary and plowing operations, respectively. The experimental results may provide a general guideline for evaluating the tillage workability by cone index.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.276-276
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• 2021

최근 댐과 같은 수공구조물의 건설로 대규모 홍수피해는 급격히 줄어들었지만, 돌발홍수(flash flood)로 인한 저지대 침수 등의 도시홍수 발생빈도가 급증하고 있다. 2020년에는 최장의 장마가 관측되었으며, 전국적으로 홍수로 인한 침수피해가 발생하였다. 홍수에 선제적으로 대응하기 위해서 신뢰성 있는 홍수예·경보가 필요하며, 이를 위해서는 신속하고 정확성있는 강우예측이 선행되어야 한다. 이에 본 연구에서는 초단기 강우예측을 목적으로 둔 레이더 기반의 강우앙상블 예측모형을 개발하였다. 라그랑지안 지속성(Lagrangian persistence)을 기반으로 개발하였으며, 강우장의 이동 패턴은 이류특성을 활용해 추정하였다. 즉, 강우장의 예측정확도를 향상시키기 위해 공간적 규모별 캐스캐이드(cascade) 방법으로 분리해 이동 경로를 추정하였다. 예측시간에 따른 강우량은 각 캐스캐이드에 자기회귀모형을 적용하였다. 레이더 강우량은 2016-2020년 사이에 발생한 강우사상에 대한 환경부 홍수통제소에서 제공한 레이더 합성장을 이용하였다. 예측강우량에 대한 평가는 RMSE, Pearson's Correlation, FSS 등 통계치를 통해 수행하였다. 본 연구에서 소개된 강우예측 모형은 초단기 홍수예측에 정확도 높은 강우 정보를 제공할 수 있으며, 이에 따라 홍수피해를 저감하는데 도움이 될 것으로 판단된다.