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.
초록·키워드
Model evaluation is essential for ensuring the reliability, transparency, and practical usefulness of simulations in energy systems, environmental modeling, and sustainability science. However, the frequent misuse, misinterpretation, and isolated application of common performance metrics particularly root mean square error (RMSE), mean absolute percentage error (MAPE), and R 2 continue to undermine model credibility and lead to misleading conclusions. This study (i) reviews and defines five widely used evaluation metrics (mean absolute error [MAE], RMSE, mean bias error [MBE], MAPE, and R 2 ), including their mathematical foundations and conceptual meaning, (ii) compares their characteristics, strengths, and limitations, and (iii) provides context‐specific guidelines for selecting appropriate indicators based on modeling objectives and data behavior. Using a combined methodology consisting of a structured literature review, a controlled illustrative dataset, and sensitivity analysis, the study demonstrates how each metric responds to outliers and zero‐observation values. Results show that RMSE and MAE increase more under outlier conditions, MAPE becomes undefined when observed values reach zero, and R 2 collapses from 0.82 to below 0.05 when data irregularities are introduced. These findings highlight critical vulnerabilities that can distort evaluation if metrics are used in isolation. The study contributes a consolidated comparative framework, practical recommendations for multimetric reporting, and guidance for improving evaluation transparency in energy, environmental, and sustainability applications. Adopting a context‐aware, multimetric strategy enhances credibility, interpretability, and policy relevance of model‐based assessments in support of sustainable development goals (SDGs).
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.
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