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Water resource forecasting with machine learning and deep learning : A scientometric analysis
Liu, Chan-Juan Xu, Jing Li, Xi-An Yu, Zhongyao Wu, Jinran
Liu, Chan-Juan
Xu, Jing
Li, Xi-An
Yu, Zhongyao
Wu, Jinran
Abstract
Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging themes, this study analyzed 876 articles published between 2015 and 2022, retrieved from the Web of Science database. Leveraging CiteSpace visualization software, bibliometric techniques, and literature review methodologies, the investigation identified essential literature related to water prediction using machine learning and deep learning approaches. Through a comprehensive analysis, the study identified significant countries, institutions, authors, journals, and keywords in this field. By exploring this data, the research mapped out prevailing trends and cutting-edge areas, providing valuable insights for researchers and practitioners involved in water prediction through machine learning and deep learning. The study aims to guide future inquiries by highlighting key research domains and emerging areas of interest.
Keywords
Water forecasting, Machine learning/deep learning, Web of Science, Visualization
Date
2024
Type
Journal article
Journal
Book
Volume
5
Issue
Page Range
1-12
Article Number
ACU Department
Institute for Positive Psychology and Education
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Event URL
Open Access Status
Open access
License
CC BY-NC-ND 4.0
File Access
Open
Notes
© 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).