Research Progress of Hydrological Big Data Based on CiteSpace Knowledge Graph
Research related to hydrological big data has been a focal point and core issue in the field of hydrology in recent years.It is also an important component for improving the efficiency of hydrological affairs processing and enhancing the authenticity and credibility of hydrological patterns.This stu...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2024-01-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.02.005 |
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| Summary: | Research related to hydrological big data has been a focal point and core issue in the field of hydrology in recent years.It is also an important component for improving the efficiency of hydrological affairs processing and enhancing the authenticity and credibility of hydrological patterns.This study utilized a sample dataset comprising 264 papers collected from China Knowledge Infrastructure (CNKI) and 219 papers collected from Web of Science (WOS).Using CiteSpace software,this paper analyzed the researchers,institutions, and research hotspots and explored the development trend of research in this field in depth.The findings indicate that,overall,both Chinese and international publications show an increasing trend.Regarding researchers and research institutions,there is a phenomenon of “large scattering and small gathering” among Chinese scholars and institutions.Examining research hotspots reveals that keywords such as “intelligent hydrology,”“early warning system,”“big data testing” signify that the focus of research in this field is gradually shifting towards technological and digital directions.Whether domestically or internationally,modern hydrological monitoring technologies and hydrological methods,in comparison to traditional technologies and methods,demonstrate higher accuracy and stability.They can more fully meet the practical application requirements,and the combination of hydrology and big data has gradually become a research trend in this field. |
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| ISSN: | 1001-9235 |