Earthquake prediction is one of the important themes of earthquake research, and it is also a very difficult scientific problem in the world. In this study, a bibliometric analysis is conducted on the scientific publications about earthquake prediction indexed in SCIE (Science Citation Index Expanded) and SSCI (Social Sciences Citation Index) databases during the past two decades (1998–2017). The subject categories, annual and journal distributions, leading countries/regions and institutions are investigated in this field. The main research topics are identified through text mining method. The research trends are explored by keyword co-occurrence analysis and bursting keywords detection techniques. The results of this study are helpful for scholars in this field to find the knowledge structure and important participants. It is also helpful for scholars to seize the current research hotspots and future development trends in this field.
HuangY. and YuM., Review of soil liquefaction characteristics during major earthquakes of the twenty-first century, Natural Hazards65(3) (2013), 2375–2384.
2.
XiongM., MengX., WangS., GuoP., LiY., ChenG...., ZhaoY., Effectiveness of debris flow mitigation strategies in mountainous regions, Progress in Physical Geography40(6) (2016), 768–793.
3.
DjerbalL., KhoudiI., AlimrinaN., MelbouciB. and BaharR., Assessment and mapping of earthquake-induced landslides in Tigzirt City, Algeria, Natural Hazards87(3) (2017), 1859–1879.
4.
MassonnetD., RossiM., CarmonaC., AdragnaF., PeltzerG., FeiglK. and RabauteT., The displacement field of the Landers earthquake mapped by radar interferometry, Nature364 (1993), 138–142.
5.
TowhataI., Geotechnical earthquake engineering, Springer Science & Business Media, (2008).
6.
CuiP., ZouQ., XiangL.Z. and ZengC., Risk assessment of simultaneous debris flows in mountain townships, Progress in Physical Geography37(4) (2013), 516–542.
7.
CaseB.S. and HaleR.J., Using novel metrics to assess biogeographic patterns of abrupt treelines in relation to abiotic influences, Progress in Physical Geography39(3) (2015), 310–336.
8.
AhsanN. and Tullio-PowS., Functional clothing for natural disaster survivors, Disaster Prevention and Management24(3) (2015), 306–319.
9.
KhanR., NguyenT.H., ShislerJ., LinL.S., JutlaA. and ColwellR., Evaluation of Risk of Cholera after a Natural Disaster: Lessons Learned from the Nepal Earthquake, Journal of Water Resources Planning and Management144(8) (2018), 04018044.
10.
DenolleM.A., DunhamE.M., PrietoG.A. and BerozaG.C., Strong ground motion prediction using virtual earthquakes, Science343 (2014), 399–403.
11.
CastigliaM. and de MagistrisF.S., Prediction of the number of equivalent cycles for earthquake motion, Bulletin of Earthquake Engineering16(9) (2018), 3571–3603.
12.
XuY., RenT., LiuY. and LiZ., Earthquake prediction based on community division, Physica A: Statistical Mechanics and its Applications506 (2018), 969–974.
13.
WeiL., GuoJ., LiuJ., LuZ., LiH. and CaiH., Satellite thermal infrared earthquake precursor to the Wenchuan Ms 8.0 earthquake in Sichuan, China, and its analysis on geo-dynamics, Acta Geologica Sinica-English Edition83(4) (2009), 767–775.
14.
EftaxiasK., KapirisP., PolygiannakisJ., PeratzakisA., KopanasJ., AntonopoulosG. and RigasD., Experience of short term earthquake precursors with VLF–VHF electromagnetic emissions, Natural Hazards and Earth System Sciences3(3/4) (2003), 217–228.
15.
ZhangX.H., RenX.D., WuF.B. and ChenY.Y., A new method for detection of pre-earthquake ionospheric anomalies, Chinese Journal of Geophysics56(2) (2013), 213–222.
16.
BorghiA., AoudiaA., RivaR.E. and BarzaghiR., GPS monitoring and earthquake prediction: a success story towards a useful integration, Tectonophysics465(1-4) (2009), 177–189.
17.
KagabuM., IdeK., HosonoT., NakagawaK. and ShimadaJ., Describing coseismic groundwater level rise using tank model in volcanic aquifers, Kumamoto, southern Japan, Journal of Hydrology582 (2020), 124464.
18.
GitisV., DerendyaevA. and PetrovK., Analyzing the Performance of GPS Data for Earthquake Prediction, Remote Sensing13(9) (2021), 1842.
19.
GajanS., Application of machine learning algorithms to performance prediction of rocking shallow foundations during earthquake loading, Soil Dynamics and Earthquake Engineering151 (2021), 106965.
20.
LiY., CuiP., YeC., JuniorJ.M., ZhangZ., GuoJ. and LiJ., Accurate prediction of earthquake-induced landslides based on deep learning considering landslide source area, Remote Sensing13(17) (2021), 3436.
21.
RajabiA.M., KhodaparastM. and MohammadiM., Earthquake-induced landslide prediction using backpropagation type artificial neural network: case study in northern Iran, Natural Hazards, (2021). https://doi.org/10.1007/s11069-021-04963-8.
22.
UchidaN. and BürgmannR., A Decade of Lessons Learned from the Tohoku-Oki Earthquake, Reviews of Geophysics59(2) (2021), e2020RG000713.
LiuX., ZhanF.B., HongS., NiuB. and LiuY., A bibliometric study of earthquake research: 1900–2010, Scientometrics92(3) (2012), 747–765.
25.
MerigóJ.M., Mas-TurA., Roig-TiernoN. and Ribeiro-SorianoD., A bibliometric overview of the Journal of Business Research between 1973 and 2014, Journal of Business Research68(12) (2015), 2645–2653.
26.
YuD.J., XuZ.S. and WangW.R., Bibliometric analysis of fuzzy theory research in China: A 30-year perspective, Knowledge-Based Systems141 (2018), 188–199.
27.
Abad-SeguraE. and González-ZamarM.D., Research analysis on emerging technologies in corporate accounting, Mathematics8(9) (2020), 1589.
28.
ZuritaG., MerigóJ.M., Lobos-OssandónV. and Mulet-FortezaC., Leading countries in computer science: A bibliometric overview, Journal of Intelligent & Fuzzy Systems40(2) (2021), 1957–1970.
29.
YuD.J., KouG., XuZ. and ShiS., Analysis of collaboration evolution in AHP research: 1982–2018, International Journal of Information Technology & Decision Making20(01) (2021), 7–36.
30.
Avilés-OchoaE., Flores-SosaM. and MerigóJ.M., A bibliometric overview of volatility, Journal of Intelligent & Fuzzy Systems40(2) (2021), 1997–2009.
31.
YuD.J., XuZ.S. and WangX., Bibliometric analysis of support vector machines research trend: a case study in China, International Journal of Machine Learning and Cybernetics11(3) (2020), 715–728.
32.
ZhangS.L. and AnP.J., A bibliometric analysis of the development status of international seismological research, Acta Geoscientica Sinica33(3) (2012), 371–378.
33.
HirschJ.E., An index to quantify an individual’s scientific research output, Proceedings of the National academy of Sciences102(46) (2005), 16569–16572.
34.
BornmannL., WallonG. and LedinA., Is the h index related to (standard) bibliometric measures and to the assessments by peers? An investigation of the h index by using molecular life sciences data, Research Evaluation17(2) (2008), 149–156.
35.
YuD.J. and ChenY., Dynamic structure and knowledge diffusion trajectory research in green supply chain, Journal of Intelligent & Fuzzy Systems40(3) (2021), 4979–4991.
36.
HirschJ.E., Does the h index have predictive power?Proceedings of the National Academy of Sciences104(49) (2007), 19193–19198.
37.
BornmannL. and DanielH.D., Does the h-index for ranking of scientists really work?Scientometrics65(3) (2005), 391–392.
38.
YuD.J., XuZ.S., PedryczW. and WangW., Information Sciences 1968–2016: a retrospective analysis with text mining and bibliometric, Information Sciences418 (2017), 619–634.
39.
ChenC., CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature, Journal of the American Society for Information Science and Technology57(3) (2006), 359–377.
40.
ChenC., Ibekwe-SanJuanF. and HouJ., The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis, Journal of the American Society for information Science and Technology61(7) (2010), 1386–1409.
41.
OsińskiS. and WeissD., Carrot 2: Design of a flexible and efficient web information retrieval framework, In International Atlantic Web Intelligence Conference (pp. 439–444), Springer, Berlin, Heidelberg, (2005).
42.
StefanowskiJ. and WeissD., Carrot 2 and language properties in web search results clustering, In International Atlantic Web Intelligence Conference (pp. 240–249). Springer, Berlin, Heidelberg, (2003).
43.
ChenC., SongI.Y., YuanX. and ZhangJ., The thematic and citation landscape of data and knowledge engineering (1985–2007), Data & Knowledge Engineering67(2) (2008), 234–259.
44.
ChenC., HuZ., LiuS. and TsengH., Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace, Expert Opinion on Biological Therapy12(5) (2012), 593–608.
45.
ZhangJ., JiangL., LiuZ., LiY., LiuK., FangR...., LiF., A bibliometric and visual analysis of indoor occupation environmental health risks: Development, hotspots and trend directions, Journal of Cleaner Production 126824, (2021).
