Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Analysis of Publication Characteristics
3.1.1. Publication Distribution Characteristics
3.1.2. Publication Source Characteristics
3.1.3. Publication Keyword Characteristics
3.2. Analysis of Authors, Institutions, and Countries
3.2.1. Most Productive Authors in AI Research in the Field of Geohazards
3.2.2. Most Productive Institutions in Terms of AI Research in the Field of Geohazards
3.2.3. Top Countries in Terms of AI Research in the Field of Geohazards
3.3. Identification of Salient Research Clusters
3.4. Top Algorithms and Future Trends in AI Research of Geohazards
4. Future Directions
4.1. Establishment of Benchmark Database
4.2. Integration of AI with Physical Processes
4.3. Auto ML
4.4. Uncertainty Quantification
4.5. Interpretable AI
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
References
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No. | Source | Total Papers | Total Citations | Average Citations per Paper | Percentage of Total Papers |
---|---|---|---|---|---|
1 | Natural Hazards (https://www.springer.com/journal/11069, accessed on 3 September 2022) | 550 | 12,286 | 22.34 | 5.96% |
2 | Remote Sensing (https://www.mdpi.com/journal/remotesensing, accessed on 3 September 2022) | 423 | 6376 | 15.07 | 4.58% |
3 | Journal of Hydrology (https://journals.elsevier.com/journal-of-hydrology, accessed on 3 September 2022) | 314 | 13,417 | 42.73 | 3.40% |
4 | Bulletin of Earthquake Engineering (https://www.springer.com/journal/10518/, accessed on 3 September 2022) | 306 | 6068 | 19.83 | 3.32% |
5 | Soil Dynamics and Earthquake Engineering (https://www.sciencedirect.com/journal/soil-dynamics-and-earthquake-engineering, accessed on 3 September 2022) | 306 | 4060 | 13.27 | 3.32% |
6 | Environmental Earth Sciences (https://www.springer.com/journal/12665, accessed on 3 September 2022) | 298 | 7798 | 26.17 | 3.23% |
7 | Engineering Geology (https://www.sciencedirect.com/journal/engineering-geology, accessed on 3 September 2022) | 290 | 11,417 | 39.37 | 3.14% |
8 | Geomorphology (www.elsevie r.com/locate/geomorph, accessed on 3 September 2022) | 287 | 17,656 | 61.52 | 3.11% |
9 | Catena (www.elsevier.com/locate/catena, accessed on 3 September 2022) | 269 | 12,810 | 47.62 | 2.92% |
10 | Natural Hazards and Earth System Sciences (https://www.natural-hazards-and-earth-system-sciences.net/, accessed on 3 September 2022) | 264 | 6611 | 25.04 | 2.86% |
11 | Landslides (https://www.springer.com/journal/10346, accessed on 3 September 2022) | 262 | 8970 | 34.24 | 2.84% |
12 | Arabian Journal of Geosciences (https://www.springer.com/journal/12517, accessed on 3 September 2022) | 243 | 3298 | 13.57 | 2.63% |
13 | Earthquake Engineering & Structural Dynamics (https://onlinelibrary.wiley.com/journal/10969845, accessed on 3 September 2022) | 233 | 7756 | 33.29 | 2.53% |
14 | Earthquake Spectra (https://journals.sagepub.com/home/eqs, accessed on 3 September 2022) | 213 | 10,776 | 50.59 | 2.31% |
15 | Geophysical Research Letters (https://agupubs.onlinelibrary.wiley.com/journal/19448007, accessed on 3 September 2022) | 213 | 5269 | 24.74 | 2.31% |
No. | Name | Institution, Country | Total Papers | Total Citations | Average Citations per Paper | H-Index | Related Citations Impact |
---|---|---|---|---|---|---|---|
1 | Pradhan, Biswajeet | University of Technology Sydney, Australia | 136 | 13,146 | 96.66 | 94 | 1.39 |
2 | Dieu Tien Bui | University of South-Eastern Norway, Norway | 70 | 5604 | 80.06 | 68 | 1.16 |
3 | Pourghasemi, Hamid Reza | Shiraz University, Iran | 65 | 6332 | 97.42 | 66 | 1.41 |
4 | Chen, Wei | Xi’an University of Science and Technology, China | 63 | 4104 | 65.14 | 53 | 0.94 |
5 | Lee, Saro | Korea Institute of Geoscience and Mineral Resources, KIGAM, Korea | 63 | 3880 | 61.59 | 64 | 0.89 |
6 | Hong, Haoyuan | Universität Vienna, Austria | 52 | 3637 | 69.94 | 45 | 1.01 |
7 | Binh Thai Pham | University of Transport Technology, Vietnam | 45 | 2851 | 63.36 | 26 | 0.91 |
8 | Arabameri, Alireza | Tarbiat Modares University, Iran | 32 | 684 | 21.38 | 29 | 0.31 |
9 | Bradley, Brendon A. | University of Canterbury, New Zealand | 32 | 583 | 18.22 | 36 | 0.26 |
10 | Xu, Chong | Institute of Geology, China Earthquake Administration, China | 31 | 1693 | 54.61 | 29 | 0.79 |
11 | Shahabi, Himan | University of Kurdistan, Iran | 30 | 2538 | 84.60 | 52 | 1.22 |
12 | Xu, Qiang | Chengdu University of Technology, China | 30 | 659 | 21.97 | 43 | 0.32 |
13 | Rahmati, Omid | Agricultural Research, Education and Extension Organization (AREEO), Vietnam | 29 | 2231 | 76.93 | 36 | 1.11 |
14 | Prakash, Indra | Bhaskaracharya Institute for Space Applications and Geoinformatics, India | 28 | 1551 | 55.39 | 37 | 0.80 |
15 | Blaschke, Thomas | Universität Salzburg, Austria | 26 | 1235 | 47.50 | 50 | 0.69 |
No. | Institution | Total Papers | Total Citations | Average Citations per Paper | Relate Citations Impact |
---|---|---|---|---|---|
1 | Chinese Academy of Sciences (https://english.cas.cn, accessed on 3 September 2022) | 384 | 9088 | 23.67 | 0.83 |
2 | China University of Geosciences (https://en.cug.edu.cn, accessed on 3 September 2022) | 158 | 3303 | 20.91 | 0.73 |
3 | U.S. Geological Survey (https://www.usgs.gov, accessed on 3 September 2022) | 156 | 8928 | 57.23 | 2.00 |
4 | University of Chinese Academy of Sciences (https://english.ucas.ac.cn, accessed on 3 September 2022) | 115 | 1264 | 10.99 | 0.38 |
5 | Chengdu University of Technology (http://www.cdut.edu.cn. accessed on 3 September 2022) | 101 | 1549 | 15.34 | 0.54 |
6 | Tongji University (https://en.tongji.edu.cn, accessed on 3 September 2022) | 100 | 1749 | 17.49 | 0.61 |
7 | University of California, Berkeley (https://www.berkeley.edu, accessed on 3 September 2022) | 97 | 4938 | 50.91 | 1.78 |
8 | University of Technology Sydney (https://www.uts.edu.au, accessed on 3 September 2022) | 93 | 2583 | 27.77 | 0.97 |
9 | Duy Tan University (https://duytan.edu.vn, accessed on 3 September 2022) | 91 | 3278 | 36.02 | 1.26 |
10 | University of Tehran (https://ut.ac.ir/en. accessed on 3 September 2022) | 88 | 2027 | 23.03 | 0.81 |
11 | Islamic Azad University (https://iau.ae, accessed on 3 September 2022) | 86 | 3074 | 35.74 | 1.25 |
12 | China Earthquake Administration (https://www.cea.gov.cn, accessed on 3 September 2022) | 85 | 2431 | 28.60 | 1.00 |
13 | Sejong University (https://en.sejong.ac.kr/eng/index.do, accessed on 3 September 2022) | 85 | 3640 | 42.82 | 1.50 |
14 | Tarbiat Modares University (https://en.modares.ac.ir, accessed on 3 September 2022) | 84 | 2309 | 27.49 | 0.96 |
15 | Kyoto University (https://www.kyoto-u.ac.jp/en, accessed on 3 September 2022) | 83 | 1238 | 14.92 | 0.52 |
Country | Total Papers | Total Citations | Average Citations per Paper | Closest Collaborating Country | Number of Total Collaborators |
---|---|---|---|---|---|
China | 2349 | 41,179 | 17.53 | United States | 279 |
United States | 1993 | 61,656 | 30.94 | China | 279 |
Italy | 894 | 27,388 | 30.64 | United States | 72 |
Iran | 629 | 19,302 | 30.69 | Vietnam | 85 |
England | 572 | 18,371 | 32.12 | United States | 111 |
Japan | 505 | 11,036 | 21.85 | China | 83 |
India | 504 | 9430 | 18.71 | Vietnam | 63 |
Australia | 443 | 11,376 | 25.68 | China | 100 |
Germany | 410 | 11,086 | 27.04 | United States | 51 |
Canada | 409 | 14,586 | 35.66 | United States | 78 |
France | 386 | 10,326 | 26.75 | United States | 51 |
South Korea | 323 | 10,793 | 33.41 | Australia | 65 |
Turkey | 289 | 9311 | 32.22 | United States | 34 |
Spain | 277 | 7582 | 27.37 | Italy | 47 |
Switzerland | 240 | 7625 | 31.77 | United States | 42 |
Netherlands | 239 | 9496 | 39.73 | United States | 35 |
Vietnam | 182 | 7529 | 41.37 | Iran | 85 |
Greece | 174 | 4913 | 28.24 | Italy | 26 |
Malaysia | 171 | 12,946 | 75.71 | Iran | 52 |
Norway | 162 | 9103 | 56.19 | Vietnam | 45 |
Cluster ID | Size | Silhouette | Cluster Label | Representative Document |
---|---|---|---|---|
#0 | 70 | 0.81 | Ground motion | Boore & Atkinson [40] |
#1 | 64 | 0.91 | Deep learning | Bui et al. [41] |
#2 | 61 | 0.97 | GIS | Guzzetti et al. [42] |
#3 | 54 | 0.91 | Landslide | Pradhan [43] |
#4 | 47 | 0.92 | Impact | Kim et al. [44] |
1 | Naive Bayes | |
Summary | NB classifiers are simple probabilistic classifiers based on the Bayes theorem and the strong (naive) independence assumption between features [52]. | |
Advantages | ||
Limitations | ||
2 | Decision Tree | |
Summary | The DT is a basic classification and regression method. The DT model has a tree-like structure and represents the process of classifying instances based on features in a classification problem [58]. | |
Advantages | ||
Limitations | ||
3 | Support Vector Machine | |
Summary | SVM is a supervised learning machine proposed by Vapnik et al. [63,64]. It is a powerful tool for solving pattern classification problems and regression problems [65] and has been used in various fields [66,67,68]. | |
Advantages | ||
Limitations | ||
4 | Artificial Neural Networks | |
Summary | ANNs are algorithmic models inspired by biological neural networks. They are massively parallel systems with a large number of interconnected simple processors [72]. | |
Advantages | ||
Limitations | ||
5 | Extreme Learning Machine | |
Summary | The ELM is a single-layer feedforward neural network that overcomes the difficulty of parameter initialization. It is one of the most widely used algorithms for predicting time series data [76]. | |
Advantages | ||
Limitations | ||
6 | K-Nearest Neighbor | |
Summary | KNN is a nonparametric method that is considered one of the top 10 data mining algorithms because of its simplicity, efficiency, and implementation power for classification [79]. | |
Advantages | ||
Limitations | ||
7 | Logistics Regression | |
Summary | LR analysis is a statistical technique for analyzing the relationship between an independent variable and two dependent variables (dichotomous variables) and is widely used in various fields [82,83]. | |
Advantages | ||
Limitations | ||
8 | Ensemble Methods | |
Summary | EMs refer to the combination of individual AI models into one model that has higher accuracy and stronger generalization ability than the individual AI models [88,89]. | |
Advantages |
| |
Limitations | ||
9 | Deep Learning | |
Summary | DL is a branch of machine learning based on ANN [96]. It has excellent performance in processing a large amount of high level data and has a wide range of applications in various fields [97,98,99]. | |
Advantages | ||
Limitations |
|
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Jiang, S.; Ma, J.; Liu, Z.; Guo, H. Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research. Sensors 2022, 22, 7814. https://doi.org/10.3390/s22207814
Jiang S, Ma J, Liu Z, Guo H. Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research. Sensors. 2022; 22(20):7814. https://doi.org/10.3390/s22207814
Chicago/Turabian StyleJiang, Sheng, Junwei Ma, Zhiyang Liu, and Haixiang Guo. 2022. "Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research" Sensors 22, no. 20: 7814. https://doi.org/10.3390/s22207814
APA StyleJiang, S., Ma, J., Liu, Z., & Guo, H. (2022). Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research. Sensors, 22(20), 7814. https://doi.org/10.3390/s22207814