인공신경망을 이용한 해양관측자료 보간 기법 소개

• 영문명: Marine Data Interpolation Using Neural Networks
• 발행기관: 전남대학교 수산과학연구소
• 저자명: 주현석(Hyun Seok Joo) 김국진(Kuk Jin Kim) 한진현(Jin Hyun Han) 김영택(Young Taeg Kim)
• 간행물 정보: 『수산과학연구소 논문집』제28권 제2호, 126~132쪽, 전체 7쪽
• 주제분류: 농수해양 > 수산학
• 파일형태: PDF
• 발행일자: 2019.12.31

영문 초록

In the framework of “The Comprehensive Data Analysis for Areas under the National Jurisdiction” Project, the Korea Hydrographic and Oceanographic Agency first checks data for missing or wrong values, then provides the data to the public. For stationary observation points, removing wrong or missing values entails temporal discontinuity which may not be best restored with interpolation. Manual correction is time-consuming and the decision criteria may differ among employees. The less is the quality of the data, the more specialists are to be involved, and the less objective are the models’ outcomes that use the data as an input. The focus of the current study is to overcome this by a temporal and spatial interpolation of marine data using neural networks. The experiments were done in two cases: an interpolation based on the single observation point data, and an interpolation based on the neighborhood observation points’ data. Then, each case was further subdivided into an interpolation based on six and twelve-hours time horizons. This is because, the variation in data is not large for the data within an hour timeframe, so simple nearest interpolation or linear interpolation models are sufficient. On the basis of this, the feasibility of interpolation of missing marine data was verified.

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Neural networks Machine learning Deep learning Marine data Interpolation