Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research
Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/20/7814 |
| _version_ | 1797470008107859968 |
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| author | Sheng Jiang Junwei Ma Zhiyang Liu Haixiang Guo |
| author_facet | Sheng Jiang Junwei Ma Zhiyang Liu Haixiang Guo |
| author_sort | Sheng Jiang |
| collection | DOAJ |
| description | Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields. |
| first_indexed | 2024-03-09T19:30:40Z |
| format | Article |
| id | doaj.art-5e45b7bd77e74c72a426e9c0a54e0679 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:30:40Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5e45b7bd77e74c72a426e9c0a54e06792023-11-24T02:26:36ZengMDPI AGSensors1424-82202022-10-012220781410.3390/s22207814Scientometric Analysis of Artificial Intelligence (AI) for Geohazard ResearchSheng Jiang0Junwei Ma1Zhiyang Liu2Haixiang Guo3Badong National Observation and Research Station of Geohazards (BNORSG), China University of Geosciences, Wuhan 430074, ChinaBadong National Observation and Research Station of Geohazards (BNORSG), China University of Geosciences, Wuhan 430074, ChinaBadong National Observation and Research Station of Geohazards (BNORSG), China University of Geosciences, Wuhan 430074, ChinaSchool of Economics and Management, China University of Geosciences, Wuhan 430074, ChinaGeohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields.https://www.mdpi.com/1424-8220/22/20/7814geohazardartificial intelligence (AI)scientometricvisualizationresearch cluster |
| spellingShingle | Sheng Jiang Junwei Ma Zhiyang Liu Haixiang Guo Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research Sensors geohazard artificial intelligence (AI) scientometric visualization research cluster |
| title | Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research |
| title_full | Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research |
| title_fullStr | Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research |
| title_full_unstemmed | Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research |
| title_short | Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research |
| title_sort | scientometric analysis of artificial intelligence ai for geohazard research |
| topic | geohazard artificial intelligence (AI) scientometric visualization research cluster |
| url | https://www.mdpi.com/1424-8220/22/20/7814 |
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