• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.3
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• pp.127-134
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• 2010

This study investigated the accuracy of mean sea level pressure(MSLP) predicted by weather models around Korean Peninsula during typhoon Manyi and Usagi period in 2007. The mesoscale regional model, RDAPS, KWRF with 30 and 10 km horizontal resolution and developed high-resolution WRF models with 9 and 3 km horizontal resolutions are used to predict the features of MSLP. The predicted MSLP aspects were verified using observed results from total 35 coastal stations including AWS and ocean buoy. Although 4 models showed the reasonable MLSP results during typhoon periods, the highest resolution, 3km WRF model show the most accurate MSLP results with maximum 69% and 60% improvement with comparisons of RDAPS and KWRF, respectively.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.5
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• pp.467-475
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• 2007

This study compared the sea surface wind pattern between model results from KMA operational model (RDAPS) and observational results from QuikSCAT in the 2005-2006 year. The mean spatial distributions of sea surface wind show the prominent seasonal patterns of summer and winter season adjacent to Korean Peninsular. The statistical analysis also shows well seasonal variation of sea surface wind patterns between model and observation results. The BIAS value represents less than -0.5 m/s and -1 m/s in summer and winter seasons, respectively. The spatially averaged correlation coefficient shows larger than 0.7 and 0.8 in summer and winter seasons, respectively. The correlation coefficient of winter season shows higher value than that of summer season in the comparison between model and observation. This results show that the RDAPS model simulate well strong sea surface wind in winter season rather than weak sea surface wind in summer season.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1141-1144
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• 2009

인공신경망(artificial neural network) 기법은 인간의 두뇌 신경세포의 활동을 모형화한 것으로 오랜 시간동안 발전해 왔으며 여러 분야에서 활용되고 있고 수문분야에서도 인공신경망을 이용한 연구가 활발히 진행되어 왔다. RDAPS와 같은 단기수치예보 자료는 강우의 유무 판단과 같은 정성적인 분석에서 비교적 정확도가 높지만 정확한 강우량의 추정과 같은 정량적인 부분에서는 정확도가 매우 낮으므로 인공신경망 기법과 같은 후처리 기법을 통해서 정확도를 높이게 된다. 인공신경망 기법을 수행할 때, 가장 중요한 것은 입력변수선택(input variable selection)으로 입력 변수의 적절한 선택이 결과값에 큰 영향을 주게 된다. 본 연구에서는 mutual information을 입력 변수 선택 기법으로 채택하여, 인공신경망의 입력변수 선정의 정확도를 알아보고자 한다. Mutual information은 주어진 자료의 엔트로피값을 이용하여 변수들 간의 독립과 종속의 관계를 나타내는 기법으로서, MI값은 '0'에서 '1'의 값을 가지며 '0'에 가까울수록 변수들 간의 관계가 독립적이고 '1'에 가까울수록 종속적인 관계를 나타낸다. 인공신경망의 입력변수선정에 대한 mutual information의 정확도를 알아보기 위해, 기존 입력변수선택 기법과 mutual information을 이용했을 경우의 인공신경망의 처리능력, 정확도를 비교 검토하였다.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.81-94
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• 2010

Input variable selection is one of the various techniques for improving the performance of artificial neural network. In this study, mutual information is applied for input variable selection technique instead of correlation coefficient that is widely used. Among 152 variables of RDAPS (Regional Data Assimilation and Prediction System) output results, input variables for artificial neural network are chosen by computing mutual information between rainfall records and RDAPS' variables. At first the rainfall forecast variable of RDAPS result, namely APCP, is included as input variable and the other input variables are selected according to the rank of mutual information and correlation coefficient. The input variables using mutual information are usually those variables about wind velocity such as D300, U925, etc. Several statistical error estimates show that the result from mutual information is generally more accurate than those from the previous research and correlation coefficient. In addition, the artificial neural network using input variables computed by mutual information can effectively reduce the relative errors corresponding to the high rainfall events.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.51-56
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• 2006

Biases embedded in numerical weather precipitation forecasts by the RDAPS model was determined, quantified and corrected. The ultimate objective is to eventually enhance the reliability of reservoir operation by Korean Water Resources Corporation (KOWACO), which is based on precipitation-driven forecasts of stream flow. Statistical post-processing, so called MOS (Model Output Statistics) was applied to RDAPS to improve their performance. The Artificial Neural Nwetwork (ANN) model was applied for 4 cases of 'Probability of Precipitation (PoP) for wet and dry season' and 'Quantitative Precipitation Forecasts (QPF) for wet and dry season'. The reduction on the large systematic bias was especially remarkable. The performance of both networks may be improved by retraining, probably every month. In addition, it is expected that performance of the networks will improve once atmospheric profile data are incorporated in the analysis. The key to the optimal performance of ANN is to have a large data set relevant to the predictand variable. The more complex the process to be modeled by the ANN, the larger the data set needs to be.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1063-1067
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• 2008

최근 국지적 집중호우로 인한 인명과 재산피해가 증가하고 있는 실정이며 이러한 피해를 경감하기 위한 하나의 방책으로써 홍수예경보 시스템 구축에 관한 관심이 증가하고 있다. 기존의 홍수예보 시스템은 강우의 실제 관측치를 모형의 입력자료로 하여 홍수유출을 계산함으로 인해 예보시간이 촉박하였다. 실시간 강우를 이용하여 유출계산을 수행하고 그 결과가 위험하다고 판단될 때 홍수예경보를 하므로 집중호우와 같은 악기상 조건에서는 적용에 한계가 있다. 따라서 정확한 기상예보를 활용한 기상-수자원 연계기법을 개발하여 홍수예경보 시스템에 적용한다면 악기상 감시예측기술의 향상과 더불어 재해의 방지차원에서 매우 유용한 대책이 될 것이다. 이에 본 연구에서는 단기 예측강우의 국내유역 적용성 여부를 검토하기 위해 30km의 공간 해상도를 가진 단기지역예보모델인 RDAPS(Regional Data Assimilation and Prediction System) 강수자료를 활용하여 기상학적 및 수문학적 정확도를 분석하였으며, 이를 바탕으로 예측강수의 높은 활용성이 기대되는 실제 한강수계의 주요 댐 지점에 HEC-1 모형을 이용하여 댐 유입량을 산정하고 그 적용성을 평가하고자 한다.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2019.10a
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• pp.62-62
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• 2019

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2006.11a
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• pp.119-121
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• 2006

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).