Seismic hazard and risk assessment: a review of state-of-the-art traditional and GIS models

The historical records of earthquakes play a vital role in seismic hazard and risk assessment. During the last decade, geophysical, geotechnical, geochemical, topographical, geomorphological, geological data, and various satellite images have been collected, processed, and well-integrated into quali...

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Vydané v:	Arabian journal of geosciences Ročník 13; číslo 2
Hlavní autori:	Jena, Ratiranjan, Pradhan, Biswajeet, Beydoun, Ghassan, Al-Amri, Abdullah, Sofyan, Hizir
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2020 Springer Nature B.V
Predmet:	Comparative analysis Earth and Environmental Science Earth science Earth Sciences Earthquakes Geographic information systems Geographical information systems Geological data Geological hazards Geomorphology Geophysics Information systems Learning algorithms Machine learning Review Paper Risk assessment Satellite imagery Seismic activity Seismic hazard Seismology Spaceborne remote sensing State-of-the-art reviews GIS Risk Earthquake Hazard Remote sensing
ISSN:	1866-7511, 1866-7538
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Abstract	The historical records of earthquakes play a vital role in seismic hazard and risk assessment. During the last decade, geophysical, geotechnical, geochemical, topographical, geomorphological, geological data, and various satellite images have been collected, processed, and well-integrated into qualitative and quantitative spatial databases using geographical information systems (GIS). Various types of modeling approaches, such as traditional and GIS-based models, are used. Progressively, seismic studies can improve and modify systematic models and standardize the inventory map of earthquake-susceptible regions. Therefore, this paper reviews different approaches, which are organized and discussed on various models primarily used to create an earthquake scenario focusing on hazard and risk assessment. The reviews are divided into two major parts. The first part is the basic principles, data, and the methodology of various models used for seismic hazard and risk assessment. In the second part, a comparative analysis in terms of the limitations and strengths of the models, as well as application variability is presented. Furthermore, the paper includes the descriptions of software, data resources, and major conclusions. The main findings of this review explain that the capability of machine learning techniques regularly enhances the state of earthquake research, which will provide research opportunities in the future. The model suitability depends on the improvement of parameters, data, and methods that could help to prevent future risk. This paper will help researchers further understand the models based on their strengths, limitations, and applicability.
AbstractList	The historical records of earthquakes play a vital role in seismic hazard and risk assessment. During the last decade, geophysical, geotechnical, geochemical, topographical, geomorphological, geological data, and various satellite images have been collected, processed, and well-integrated into qualitative and quantitative spatial databases using geographical information systems (GIS). Various types of modeling approaches, such as traditional and GIS-based models, are used. Progressively, seismic studies can improve and modify systematic models and standardize the inventory map of earthquake-susceptible regions. Therefore, this paper reviews different approaches, which are organized and discussed on various models primarily used to create an earthquake scenario focusing on hazard and risk assessment. The reviews are divided into two major parts. The first part is the basic principles, data, and the methodology of various models used for seismic hazard and risk assessment. In the second part, a comparative analysis in terms of the limitations and strengths of the models, as well as application variability is presented. Furthermore, the paper includes the descriptions of software, data resources, and major conclusions. The main findings of this review explain that the capability of machine learning techniques regularly enhances the state of earthquake research, which will provide research opportunities in the future. The model suitability depends on the improvement of parameters, data, and methods that could help to prevent future risk. This paper will help researchers further understand the models based on their strengths, limitations, and applicability.
ArticleNumber	50
Author	Pradhan, Biswajeet Beydoun, Ghassan Al-Amri, Abdullah Sofyan, Hizir Jena, Ratiranjan
Author_xml	– sequence: 1 givenname: Ratiranjan surname: Jena fullname: Jena, Ratiranjan organization: The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney – sequence: 2 givenname: Biswajeet orcidid: 0000-0001-9863-2054 surname: Pradhan fullname: Pradhan, Biswajeet email: biswajeet24@gmail.com, Biswajeet.Pradhan@uts.edu.au organization: The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney – sequence: 3 givenname: Ghassan surname: Beydoun fullname: Beydoun, Ghassan organization: The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney – sequence: 4 givenname: Abdullah surname: Al-Amri fullname: Al-Amri, Abdullah organization: Dept. of Geology & Geophysics, College of Science, King Saud University – sequence: 5 givenname: Hizir surname: Sofyan fullname: Sofyan, Hizir organization: Department of Statistics, Syiah Kuala University
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ContentType	Journal Article
Copyright	Saudi Society for Geosciences 2020 2020© Saudi Society for Geosciences 2020
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SubjectTerms	Comparative analysis Earth and Environmental Science Earth science Earth Sciences Earthquakes Geographic information systems Geographical information systems Geological data Geological hazards Geomorphology Geophysics Information systems Learning algorithms Machine learning Review Paper Risk assessment Satellite imagery Seismic activity Seismic hazard Seismology Spaceborne remote sensing State-of-the-art reviews
Title	Seismic hazard and risk assessment: a review of state-of-the-art traditional and GIS models
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