Impact database application for natural and technological risk management

Impact database development and application for risk analysis and management promote the usage of self-learning computer systems with elements of artificial intelligence. Such system learning could be successful when the databases store the complete information about each event, parameters of the si...

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Published inNatural hazards and earth system sciences Vol. 20; no. 1; pp. 95 - 106
Main Authors Frolova, Nina, Larionov, Valery, Bonnin, Jean, Suchshev, Sergey, Ugarov, Alexander, Malaeva, Nataliya
Format Journal Article
LanguageEnglish
Published Katlenburg-Lindau Copernicus GmbH 13.01.2020
Copernicus Publ. / European Geosciences Union
Copernicus Publications
Subjects
Artificial intelligence
Computer models
Computer simulation
Computer systems
Data base management systems
DBMS software
Emergencies
Learning
Mathematical models
Rankings
Risk analysis
Risk assessment
Risk management
Sciences of the Universe
Simulation
Statistical analysis
Statistical methods
Online AccessGet full text
ISSN1684-9981
1561-8633
1684-9981
DOI10.5194/nhess-20-95-2020

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Abstract Impact database development and application for risk analysis and management promote the usage of self-learning computer systems with elements of artificial intelligence. Such system learning could be successful when the databases store the complete information about each event, parameters of the simulation models, the range of its application, and residual errors. Each new description included in the database could increase the reliability of the results obtained with application of simulation models. The calibration of mathematical models is the first step to self-learning of automated systems. The article describes the events' database structure and examples of calibrated computer models as applied to the impact of expected emergencies and risk indicator assessment. Examples of database statistics usage in order to rank the subjects of the Russian Federation by the frequency of emergencies of different character as well as risk indicators are given.
AbstractList Impact database development and application for risk analysis and management promote the usage of self-learning computer systems with elements of artificial intelligence. Such system learning could be successful when the databases store the complete information about each event, parameters of the simulation models, the range of its application, and residual errors. Each new description included in the database could increase the reliability of the results obtained with application of simulation models. The calibration of mathematical models is the first step to self-learning of automated systems. The article describes the events' database structure and examples of calibrated computer models as applied to the impact of expected emergencies and risk indicator assessment. Examples of database statistics usage in order to rank the subjects of the Russian Federation by the frequency of emergencies of different character as well as risk indicators are given.
Impact database development and application for risk analysis and management promote the usage of self-learning computer systems with elements of artificial intelligence. Such system learning could be successful when the databases store the complete information about each event, parameters of the simulation models, the range of its application, and residual errors. Each new description included in the database could increase the reliability of the results obtained with application of simulation models. The calibration of mathematical models is the first step to self-learning of automated systems. The article describes the events' database structure and examples of calibrated computer models as applied to the impact of expected emergencies and risk indicator assessment. Examples of database statistics usage in order to rank the subjects of the Russian Federation by the frequency of emergencies of different character as well as risk indicators are given.
Audience Academic
Author Ugarov, Alexander
Suchshev, Sergey
Malaeva, Nataliya
Frolova, Nina
Bonnin, Jean
Larionov, Valery
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Cites_doi 10.1785/gssrl.80.1.57
10.24057/2071-9388-2014-7-4-4-27
10.1007/978-90-481-9455-1_5
10.1007/s11069-017-2780-z
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10.1007/s10950-017-9726-5
10.31857/S0869-78092019523-37
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StartPage 95
SubjectTerms Artificial intelligence
Computer models
Computer simulation
Computer systems
Data base management systems
DBMS software
Emergencies
Learning
Mathematical models
Rankings
Risk analysis
Risk assessment
Risk management
Sciences of the Universe
Simulation
Statistical analysis
Statistical methods
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