Corrigendum to "Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland" [Sci. Total Environ. 954 (2024) 176256].

IF 8.2 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Science of the Total Environment Pub Date : 2024-12-15 Epub Date: 2024-11-12 DOI:10.1016/j.scitotenv.2024.177316

Jiye Lee, Dongho Kim, Seokmin Hong, Daeun Yun, Dohyuk Kwon, Robert L Hill, Feng Gao, Xuesong Zhang, Kyung Hwa Cho, Sangchul Lee, Yakov Pachepsky

引用次数: 0

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对 "SWAT 模型和深度学习模型在估算马里兰州 Tuckahoe 小河流域硝酸盐负荷方面的效率比较 "的更正[《总体环境科学》954 (2024) 176256]。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Science of the Total Environment 环境科学-环境科学

CiteScore

17.60

自引率

10.20%

发文量

8726

审稿时长

2.4 months

期刊介绍： The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.