DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yun, Seyoung | - |
dc.contributor.advisor | 윤세영 | - |
dc.contributor.author | Kim, ChangHwan | - |
dc.date.accessioned | 2022-04-27T19:32:32Z | - |
dc.date.available | 2022-04-27T19:32:32Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=948322&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/296219 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 지식서비스공학대학원, 2021.2,[iv, 39 p. :] | - |
dc.description.abstract | In this paper, a study was conducted to improve precipitation accuracy by correcting the results of the numerical model used for precipitation prediction using a deep learning model. Precipitation predictions are typically classified into short-term prediction (nowcasting) and long-term prediction. Recently, there are several studies that perform nowcasting using deep learning model. However, the precipitation prediction model using deep learning has a weakness in long-term prediction of more than a day, so the existing numerical model continues to be used. This study aims to improve the accuracy of precipitation prediction by using deep learning for long-term prediction. For this, the U-Net model, which is a deep learning model previously used for nowcasting, was used, and based on the fact that the location information of the prediction data is fixed, a layer that can learn information by terrain was added to the model. Through this, we will show that the performance is improved by correcting the precipitation prediction result of the existing numerical model. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Deep Learning▼aConvolutional Neural Network▼aSemantic Segmentation▼aPrecipitation Forecasting▼aNumerical Model Correction▼a2D Time-series Data | - |
dc.subject | 딥러닝▼a합성곱 신경망▼a시멘틱 구분▼a강수 유무 예보▼a수치 모델 보정▼a이차원 시계열 데이터 | - |
dc.title | Precipitation forecasting through numerical model correction considering the korean peninsula terrain | - |
dc.title.alternative | 한반도 지형을 고려하는 수치 모델 보정을 통한 강수 예측 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :지식서비스공학대학원, | - |
dc.contributor.alternativeauthor | 김창환 | - |
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