• 제목/요약/키워드: Ensemble model

검색결과 616건 처리시간 0.021초

단기 앙상블 예보에서 모형의 불확실성 표현: 태풍 루사 (Representation of Model Uncertainty in the Short-Range Ensemble Prediction for Typhoon Rusa (2002))

  • 김세나;임규호
    • 대기
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    • 제25권1호
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    • pp.1-18
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    • 2015
  • The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

효율적인 의료진단을 위한 앙상블 분류 기법 (Ensemble Classification Method for Efficient Medical Diagnostic)

  • 정용규;허고은
    • 한국인터넷방송통신학회논문지
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    • 제10권3호
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    • pp.97-102
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    • 2010
  • 의료 데이터 마이닝의 목적은 효율적인 알고리즘 및 기법을 통하여 각종 질병을 예측 분류하고 신뢰도를 높이는데 있다. 기존의 연구로 단일모델을 기반으로 하는 알고리즘이 존재하며 나아가 모델의 더 좋은 예측과 분류 정확도를 위하여 다중모델을 기반으로 하는 앙상블 기법을 적용한 연구도 진행되고 있다. 본 논문에서는 의료데이터의 보다 높은 예측의 신뢰도를 위하여 기존의 앙상블 기법에 사분위간 범위를 적용한 I-ENSEMBLE을 제안한다. 갑상선 기능 저하증 진단을 위한 데이터를 통해 실험 적용한 결과 앙상블의 대표적인 기법인 Bagging, Boosting, Stacking기법 모두 기존에 비해 현저하게 향상된 정확도를 나타내었다. 또한 기존 단일모델 기법과 비교하여 다중모델인 앙상블 기법에 사분위간 범위를 적용했을 때 더 뚜렷한 효과를 나타냄을 확인하였다.

기상청 고해상도 국지 앙상블 예측 시스템 구축 및 성능 검증 (Development and Evaluation of the High Resolution Limited Area Ensemble Prediction System in the Korea Meteorological Administration)

  • 김세현;김현미;계준경;이승우
    • 대기
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    • 제25권1호
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    • pp.67-83
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    • 2015
  • Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

단시간 다중모델 앙상블 바람 예측 (Wind Prediction with a Short-range Multi-Model Ensemble System)

  • 윤지원;이용희;이희춘;하종철;이희상;장동언
    • 대기
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    • 제17권4호
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    • pp.327-337
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    • 2007
  • In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

부도예측을 위한 KNN 앙상블 모형의 동시 최적화 (Investigating Dynamic Mutation Process of Issues Using Unstructured Text Analysis)

  • 민성환
    • 지능정보연구
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    • 제22권1호
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    • pp.139-157
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    • 2016
  • 앙상블 분류기란 개별 분류기보다 더 좋은 성과를 내기 위해 다수의 분류기를 결합하는 것을 의미한다. 이와 같은 앙상블 분류기는 단일 분류기의 일반화 성능을 향상시키는데 매우 유용한 것으로 알려져 있다. 랜덤 서브스페이스 앙상블 기법은 각각의 기저 분류기들을 위해 원 입력 변수 집합으로부터 랜덤하게 입력 변수 집합을 선택하며 이를 통해 기저 분류기들을 다양화 시키는 기법이다. k-최근접 이웃(KNN: k nearest neighbor)을 기저 분류기로 하는 랜덤 서브스페이스 앙상블 모형의 성과는 단일 모형의 성과를 개선시키는 데 효과적인 것으로 알려져 있으며, 이와 같은 랜덤 서브스페이스 앙상블의 성과는 각 기저 분류기를 위해 랜덤하게 선택된 입력 변수 집합과 KNN의 파라미터 k의 값이 중요한 영향을 미친다. 하지만, 단일 모형을 위한 k의 최적 선택이나 단일 모형을 위한 입력 변수 집합의 최적 선택에 관한 연구는 있었지만 KNN을 기저 분류기로 하는 앙상블 모형에서 이들의 최적화와 관련된 연구는 없는 것이 현실이다. 이에 본 연구에서는 KNN을 기저 분류기로 하는 앙상블 모형의 성과 개선을 위해 각 기저 분류기들의 k 파라미터 값과 입력 변수 집합을 동시에 최적화하는 새로운 형태의 앙상블 모형을 제안하였다. 본 논문에서 제안한 방법은 앙상블을 구성하게 될 각각의 KNN 기저 분류기들에 대해 최적의 앙상블 성과가 나올 수 있도록 각각의 기저 분류기가 사용할 파라미터 k의 값과 입력 변수를 유전자 알고리즘을 이용해 탐색하였다. 제안한 모형의 검증을 위해 국내 기업의 부도 예측 관련 데이터를 가지고 다양한 실험을 하였으며, 실험 결과 제안한 모형이 기존의 앙상블 모형보다 기저 분류기의 다양화와 예측 성과 개선에 효과적임을 알 수 있었다.

시각적 특징과 물리적 특징에 기반한 스태킹 앙상블 모델을 이용한 과일의 자동 선별 (Automatic Fruit Grading Using Stacking Ensemble Model Based on Visual and Physical Features)

  • 김민기
    • 한국멀티미디어학회논문지
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    • 제25권10호
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    • pp.1386-1394
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    • 2022
  • As consumption of high-quality fruits increases and sales and packaging units become smaller, the demand for automatic fruit grading systems is increasing. Compared to other crops, the quality of fruit is determined by visual characteristics such as shape, color, and scratches, rather than just physical size and weight. Accordingly, this study presents a CNN model that can effectively extract and classify the visual features of fruits and a perceptron that classifies fruits using physical features, and proposes a stacking ensemble model that can effectively combine the classification results of these two neural networks. The experiments with AI Hub public data show that the stacking ensemble model is effective for grading fruits. However, the ensemble model does not always improve the performance of classifying all the fruit grading. So, it is necessary to adapt the model according to the kind of fruit.

재무부실화 예측을 위한 랜덤 서브스페이스 앙상블 모형의 최적화 (Optimization of Random Subspace Ensemble for Bankruptcy Prediction)

  • 민성환
    • 한국IT서비스학회지
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    • 제14권4호
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    • pp.121-135
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    • 2015
  • Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.

TIGGE 모델을 이용한 한반도 여름철 집중호우 예측 활용에 관한 연구 (Predictability for Heavy Rainfall over the Korean Peninsula during the Summer using TIGGE Model)

  • 황윤정;김연희;정관영;장동언
    • 대기
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    • 제22권3호
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    • pp.287-298
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    • 2012
  • The predictability of heavy precipitation over the Korean Peninsula is studied using THORPEX Interactive Grand Global Ensemble (TIGGE) data. The performance of the six ensemble models is compared through the inconsistency (or jumpiness) and Root Mean Square Error (RMSE) for MSLP, T850 and H500. Grand Ensemble (GE) of the three best ensemble models (ECMWF, UKMO and CMA) with equal weight and without bias correction is consisted. The jumpiness calculated in this study indicates that the GE is more consistent than each single ensemble model. Brier Score (BS) of precipitation also shows that the GE outperforms. The GE is used for a case study of a heavy rainfall event in Korean Peninsula on 9 July 2009. The probability forecast of precipitation using 90 members of the GE and the percentage of 90 members exceeding 90 percentile in climatological Probability Density Function (PDF) of observed precipitation are calculated. As the GE is excellent in possibility of potential detection of heavy rainfall, GE is more skillful than the single ensemble model and can lead to a heavy rainfall warning in medium-range. If the performance of each single ensemble model is also improved, GE can provide better performance.

Ensemble Methods Applied to Classification Problem

  • Kim, ByungJoo
    • International Journal of Internet, Broadcasting and Communication
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    • 제11권1호
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    • pp.47-53
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    • 2019
  • The idea of ensemble learning is to train multiple models, each with the objective to predict or classify a set of results. Most of the errors from a model's learning are from three main factors: variance, noise, and bias. By using ensemble methods, we're able to increase the stability of the final model and reduce the errors mentioned previously. By combining many models, we're able to reduce the variance, even when they are individually not great. In this paper we propose an ensemble model and applied it to classification problem. In iris, Pima indian diabeit and semiconductor fault detection problem, proposed model classifies well compared to traditional single classifier that is logistic regression, SVM and random forest.

기상청 기후예측시스템(GloSea5)의 과거기후장 앙상블 확대에 따른 예측성능 평가 (Assessment of the Prediction Performance of Ensemble Size-Related in GloSea5 Hindcast Data)

  • 박연희;현유경;허솔잎;지희숙
    • 대기
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    • 제31권5호
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    • pp.511-523
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    • 2021
  • This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.