Applying a machine learning-based method for the prediction of suspended sediment concentration in the Red river basin

IF 2.7 Q3 ENVIRONMENTAL SCIENCES

Modeling Earth Systems and Environment Pub Date : 2024-01-09 DOI:10.1007/s40808-023-01915-y

Son Q. Nguyen, Linh C. Nguyen, T. Ngo‐Duc, Sylvain Ouillon

引用次数: 0

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应用基于机器学习的方法预测红河流域的悬浮泥沙浓度

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来源期刊

Modeling Earth Systems and Environment ENVIRONMENTAL SCIENCES-

CiteScore

6.30

自引率

16.70%

发文量

244

期刊介绍： The peer-reviewed journal Modeling Earth Systems and Environment (MESE) provides a unique publication platform by discussing interdisciplinary problems and approaches through modeling. The focus of MESE is on modeling in earth and environment related fields, such as: earth and environmental engineering; climate change; hydrogeology; aquatic systems and functions; atmospheric research and water; land use and vegetation change; modeling of forest and agricultural dynamics; and economic and energy systems. Furthermore, the journal combines these topics with modeling of anthropogenic or social phenomena and projections to be used by decision makers.In addition to Research Articles, Modeling Earth Systems and Environment publishes Review Articles, Letters, and Data Articles:Research Articles have a recommended length of 10-12 published pages, referees will be asked to comment specifically on the manuscript length for manuscripts exceeding this limit.Review articles provide readers with assessments of advances, as well as projected developments in key areas of modeling earth systems and the environment. We expect that a typical review article will occupy twelve to fifteen pages in journal format, and have a substantial number of citations, which justify the comprehensive nature of the review.Letters have a shorter publication time and provide an opportunity to rapidly disseminate novel results expected to have an immediate impact in the earth system and environmental modeling community. Letters should include a short abstract, should not exceed four journal pages and about 10 citations.Data Articles give you the opportunity to share and reuse each other''s datasets as electronic supplementary material. To facilitate reproducibility, you need to thoroughly describe your data, the methods of collection, and the already proceeded assimilation. Data Articles have a recommended length of 4-6 pages.Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements