• 한국항공운항학회지
• /
• 제14권4호
• /
• pp.94-100
• /
• 2006

The purpose of this case study is to determine the possibility of Numerical Forecasting of sea fog at West Sea in spring time. For practical method of analyzing the data collected from 24th to 26th March 2003, Numerical Weather Prediction model MM5(Mesoscale Model Version 5) and synoptic field study using synoptic chart, upper level chart, and sea surface temperature were employed. The results of synoptic field analysis summarized that sea fog at West sea in spring is intensified by the inflow of the warm flow from west or southwest, low sea surface temperature to increase the temperature difference between air and sea surface, and inversion layer to disturb the disperse. It appears that the possibility of sea fog forecasting by MM5, in view of the result that the MM5 output is similar to the synoptic fields analysis.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2017년도 학술발표회
• /
• pp.194-194
• /
• 2017

Flooding is one of the most serious and frequently occurred natural disaster at many regions around the world. Especially, under the climate change impact, it is more and more increasingly trend. To reduce the flood damage, flood forecast and its accuracy analysis are required. This study is conducted to analyze the accuracy of the real-time flood forecasting of a coupled meteo-hydrological model for the Han River basin, South Korea. The LDAPS (Local Data Assimilation and Prediction System) products with the spatial resolution of 1.5km and lead time of 36 hours are extracted and used as inputs for the SURR (Sejong University Rainfall-Runoff) model. Three statistical criteria consisting of CC (Corelation Coefficient), RMSE (Root Mean Square Error) and ME (Model Efficiency) are used to evaluate the performance of this couple. The results are expected that the accuracy of the flood forecasting reduces following the increase of lead time corresponding to the accuracy reduction of LDAPS rainfall. Further study is planed to improve the accuracy of the real-time flood forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권10호
• /
• pp.4887-4907
• /
• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2003년도 춘계공동학술대회논문집
• /
• pp.51-59
• /
• 2003

해양사고 예보 시스템(MCFS)은 해양사고의 예측건수와 위험수준을 일기예보와 같이 방송하기 위한 것이다. MCFS는 해양사고 수량화 D/B, 예측 모델, 3차원 통계 가시화 시스템 등으로 구성되어 있다. 이 논문에서는 수량화 D/B의 구현 절차를 기술했다. 해양사고 데이터는 1990년부터 2000년까지 11년간 위도 33$^{\circ}$N~35$^{\circ}$N와 경도124$^{\circ}$E~127$^{\circ}$E의 대한민국 서남해안 일대에서 발생한 총 724건을 수집하였다. 수량화 D/B의 분석방법을 제안하고 그 유효성을 검토하였다.

• Water Engineering Research
• /
• 제2권2호
• /
• pp.113-121
• /
• 2001

The objective of this study is to develop a prediction mode for a flood forecasting system in the downstream of the Nakdong river basin. Ranging from the gauging station at Jindong to the Nakdong estuary barrage, the hydraulic flood routing model(DWOPER) based on the Saint Venant equation was calibrated by comparing the calculated river stage with the observed river stages using four different flood events recorded. The upstream boundary condition was specified by the measured river stage data at Jindong station and the downstream boundary condition was given according to the tide level data observed at he Nakdong estuary barrage. The lateral inflow from tributaries were estimated by the rainfall-runoff model. In the calibration process, the optimum roughness coefficients for proper functions of channel reach and discharge were determined by minimizing the sum of the differences between the observed and the computed stage. In addition, the forecasting lead time on the basis of each gauging station was determined by a numerical simulation technique. Also, we suggested a model structure for a real-time flood forecasting system and tested it on the basis of past flood events. The testing results of the developed system showed close agreement between the forecasted and observed stages. Therefore, it is expected that the flood forecasting system we developed can improve the accuracy of flood forecasting on the Nakdong river.

• 한국환경과학회지
• /
• 제26권10호
• /
• pp.1167-1180
• /
• 2017

Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

• 한국물환경학회지
• /
• 제30권1호
• /
• pp.16-24
• /
• 2014

A combined watershed and receiving waterbody model was developed for operational water flow forecasting of the Nakdong river. The Hydrological Simulation Program Fortran (HSPF) was used for simulating the flow rates at major tributaries. To simulate the flow dynamics in the main stream, a three-dimensional hydrodynamic model, EFDC was used with the inputs derived from the HSPF simulation. The combined models were calibrated and verified using the data measured under different hydrometeological and hydraulic conditions. The model results were generally in good agreement with the field measurements in both calibration and verification. The 7-days forecasting performance of water flows in the Nakdong river was satisfying compared with model calibration results. The forecasting results suggested that the water flow forecasting errors were primarily attributed to the uncertainties of the models, numerical weather prediction, and water release at the hydraulic structures such as upstream dams and weirs. From the results, it is concluded that the combined watershed-waterbody model could successfully simulate the water flows in the Nakdong river. Also, it is suggested that integrating real-time data and information of dam/weir operation plans into model simulation would be essential to improve forecasting reliability.

• 한국산업정보학회논문지
• /
• 제29권3호
• /
• pp.51-65
• /
• 2024

스마트 그리드에서 전력 시스템을 효과적으로 운영하기 위해서는 전력 수요량을 정확히 예측하는 것이 중요하다. 최근 기계학습 기술의 발달로, 인공지능 기반의 전력 수요량 예측 모델이 활발히 연구되고 있다. 하지만, 기존 모델들은 모든 입력변수를 수치화하여 입력하기 때문에, 이러한 수치들 사이의 의미론적 관계를 반영하지 못해 예측 모델의 정확도가 하락할 수 있다. 본 논문은 입력 데이터에 대하여 거대언어모델을 통해 추출한 특징을 이용하여 단기 전력 수요량을 예측하는 기법을 제안한다. 먼저, 입력변수를 문장 형식의 프롬프트로 변환한다. 이후, 가중치가 동결된 거대언어모델을 이용하여 프롬프트에 대한 특징을 나타내는 임베딩 벡터를 도출하고, 이를 입력으로 받은 모델을 학습하여 예측을 수행한다. 실험 결과, 제안 기법은 수치형 데이터에 기반한 예측 모델에 비해 높은 성능을 보였고, 프롬프트에 대한 거대언어모델의 주의집중 가중치를 시각화함으로써 예측에 있어 주요한 영향을 미친 정보를 확인하였다.

• 전기학회논문지
• /
• 제59권12호
• /
• pp.2202-2211
• /
• 2010

The wind power prediction system is composed of a meteorological forecasting module, calculation module of wind power output and HMI(Human Machine Interface) visualization system. The final information from this system is a short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction value. The meteorological forecasting module for wind speed and direction forecasting is a combination of physical and statistical model. In this system, the WRF(Weather Research and Forecasting) model, which is a three-dimensional numerical weather model, is used as the physical model and the GFS(Global Forecasting System) models is used for initial condition forecasting. The 100m resolution terrain data is used to improve the accuracy of this system. In addition, optimization of the physical model carried out using historic weather data in Jeju. The mid-term prediction value from the physical model is used in the statistical method for a short-term prediction. The final power prediction is calculated using an optimal adjustment between the currently observed data and data predicted from the power curve model. The final wind power prediction value is provided to customs using a HMI visualization system. The aim of this study is to further improve the accuracy of this prediction system and develop a practical system for power system operation and the energy market in the Smart-Grid.

• 한국습지학회지
• /
• 제13권1호
• /
• pp.13-23
• /
• 2011

In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models. It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.