Jiye Lee, Dongho Kim, Seokmin Hong, Daeun Yun, Dohyuk Kwon, Robert L Hill, Feng Gao, Xuesong Zhang, Kyung Hwa Cho, Sangchul Lee, Yakov Pachepsky
{"title":"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]。","authors":"Jiye Lee, Dongho Kim, Seokmin Hong, Daeun Yun, Dohyuk Kwon, Robert L Hill, Feng Gao, Xuesong Zhang, Kyung Hwa Cho, Sangchul Lee, Yakov Pachepsky","doi":"10.1016/j.scitotenv.2024.177316","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":" ","pages":"177316"},"PeriodicalIF":8.2000,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"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].\",\"authors\":\"Jiye Lee, Dongho Kim, Seokmin Hong, Daeun Yun, Dohyuk Kwon, Robert L Hill, Feng Gao, Xuesong Zhang, Kyung Hwa Cho, Sangchul Lee, Yakov Pachepsky\",\"doi\":\"10.1016/j.scitotenv.2024.177316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":422,\"journal\":{\"name\":\"Science of the Total Environment\",\"volume\":\" \",\"pages\":\"177316\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science of the Total Environment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.scitotenv.2024.177316\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/11/12 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.scitotenv.2024.177316","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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].
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.