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
This study aims to compare the predictive performance of various models using a small-sample maritime time series dataset and to identify the key drivers of carbon dioxide (CO₂) emissions in the shipping sector through principal component analysis (PCA)-based variable contribution analysis. The dataset comprises 25 years of annual maritime cargo volumes, ship specifications, and vessel counts, with CO₂ emissions as the dependent variable. After dimensionality reduction via PCA, five predictive models —Linear Regression, Lasso, Ridge, Random Forest, and XGBoost—were applied. Model performance was evaluated using holdout validation, Leave-One-Out Cross Validation (LOOCV), and 5-fold Time Series Split cross-validation. The results show that Ridge regression achieved the highest prediction accuracy with a Mean Absolute Percentage Error (MAPE) of 1.44%, while Lasso and Linear models also demonstrated stable performance. In contrast, tree-based models suffered from overfitting in the small-sample environment, leading to performance degradation. The variable contribution analysis revealed that Gross Tonnage (TT), Container Tonnage (CT), and the number of container vessels (CF_NO) were the most influential predictors, followed by indicators related to LNG/LPG carriers and bulk carriers. This study provides a methodological framework for model selection and variable interpretation in small-sample time series contexts, contributing to both academic research and practical policy-making for carbon emission reduction in the maritime industry.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-26-02-094512495
