인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
The precipitation and snowfall in the eastern coastal region of the Korean Peninsula are high-risk meteorological phenomena often prone to heavy rain and heavy snow due to local geographic characteristics, making predictive research highly critical. However, most existing meteorological studies are limited to case studies, and the few predictive studies attempted are primarily restricted to classification based on given simple factors. Based on established meteorological findings that easterly winds are closely related to precipitation and snowfall in the eastern coastal region, this paper conducts a study to simultaneously predict precipitation and snowfall using meteorological factors of vertical layers tightly linked to easterly wind phenomena. In this paper, we construct a modified LSTM-based prediction model using ERA5 data that encompasses multiple vertical layers. We propose the application of vertical and horizontal data structures tailored to these specific meteorological characteristics, along with the processing of top-bottom relationships in vertical layer data by utilizing the sequential structure of the LSTM. Additionally, we propose a two-stage LSTM architecture designed to process low-dimensional features and short-term sequential dependencies in the first stage, while better learning high-dimensional long-term dependencies and non-linear patterns in the second stage. As a case study, we evaluated the performance of the proposed model at major locations in the Gangwon and Gyeongsang coastal regions, demonstrating superior performance to LSTM-FC in terms of precision, recall, and accuracy.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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