A Study on Construction Method of Consensus Measure Space of Macro-seismic Anomalies

DOI

10.2991/jrarc.2017.7.3.7How to use a DOI?

Keywords

Earthquake, Macro-anomaly, Measure space, Information diffusion, Internet of intelligences.

Abstract

Macro-anomalies are so complex that it is difficult to carry out the systematic quantitative analysis macro-anomalies because it is lack of measure space. It’s more difficult to find the relationship between micro-anomalies and earthquakes. In the paper, the theory system of macro-anomalies group’s measure space which is expressed by the fuzzy relationship matrix is set up. The concept of macro-anomalies group is put forward. Scene, elements and attributes are used to describe the relationship among macro-anomalies group, macro-anomalies and basic information of macro-anomalies. Based on the expression of basic information and the evaluation information, according to properties of the elements of a scene, basic indicators that describe macro-anomalies group can be calculated. The generation of results based on consensus of the first-line earthquake workers’ evaluation of macro-anomalies group’s comprehensive abnormal degree is expounded. The specific process and related algorithm is raised that use information diffusion technique calculate the fuzzy relationship matrix to express macro-anomalies group’s measure space, and it takes basic indicators of macro-anomalies group as input, the evaluation information of first-line earthquake workers as output.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Weidan Wang
AU  - Chongfu Huang
PY  - 2017
DA  - 2017/10/02
TI  - A Study on Construction Method of Consensus Measure Space of Macro-seismic Anomalies
JO  - Journal of Risk Analysis and Crisis Response
SP  - 166
EP  - 177
VL  - 7
IS  - 3
SN  - 2210-8505
UR  - https://doi.org/10.2991/jrarc.2017.7.3.7
DO  - 10.2991/jrarc.2017.7.3.7
ID  - Wang2017
ER  -