A review of operational earthquake forecasting methodologies using linguistic fuzzy rule-based models from imprecise data with weighted regression approach

Dutta P. K., . and Mishra O. P., . and Naskar M. K., . A review of operational earthquake forecasting methodologies using linguistic fuzzy rule-based models from imprecise data with weighted regression approach. pp. 220-235. ISSN 1823-8556

It is by now well recognized that earthquake disaster analysis always yields some amount of impreciseness� or vagueness� or fuzziness� due to heterogeneity in the underlying phenomenon and/or explanatory variables and/or response variable. Therefore for a more realistic modelling there is a need to incorporate this aspect in traditional models like weighted linear regression models. The present paper analytically examines some of the modern seismological earthquake algorithms used for analyzing seismo-electro-telluric-geodetic data used across the globe. The main techniques discussed are probabilistic models precursor models neural networks active fault models bayesian belief network and decision trees which provide primary solutions to the problems inherent in the prediction of earthquakes. In the study for earthquake occurence as we encounter multiple variables processes having mutual contact and mutual attributes we have devised a procedure for finding quantitative relationship estimated by missing values and coarsely discretized data value and the total error of the sample data between these variables through weighted regression.The objective of the study is interpreting the spatio-temporal properties of geographical objects with the help of regression equations and fuzzy rules for finding interconnectedness among the attributes for underlying physical phenomena of seismic behavior. We would conclude with a summary and some thoughts on future research in the area.

Full text available from: Official URL: http://jssm.umt.edu.my/files/2013/12/10W2.pdf

Abstract

It is by now well recognized that earthquake disaster analysis always yields some amount of impreciseness� or vagueness� or fuzziness� due to heterogeneity in the underlying phenomenon and/or explanatory variables and/or response variable. Therefore for a more realistic modelling there is a need to incorporate this aspect in traditional models like weighted linear regression models. The present paper analytically examines some of the modern seismological earthquake algorithms used for analyzing seismo-electro-telluric-geodetic data used across the globe. The main techniques discussed are probabilistic models precursor models neural networks active fault models bayesian belief network and decision trees which provide primary solutions to the problems inherent in the prediction of earthquakes. In the study for earthquake occurence as we encounter multiple variables processes having mutual contact and mutual attributes we have devised a procedure for finding quantitative relationship estimated by missing values and coarsely discretized data value and the total error of the sample data between these variables through weighted regression.The objective of the study is interpreting the spatio-temporal properties of geographical objects with the help of regression equations and fuzzy rules for finding interconnectedness among the attributes for underlying physical phenomena of seismic behavior. We would conclude with a summary and some thoughts on future research in the area.

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Item Type:	Article
AGROVOC Term:	Earthquakes
AGROVOC Term:	Forecasting
AGROVOC Term:	Prediction
AGROVOC Term:	Geophysics
AGROVOC Term:	Seismology
AGROVOC Term:	analysis
AGROVOC Term:	Neural networks
AGROVOC Term:	Time series analysis
AGROVOC Term:	Models
AGROVOC Term:	Mathematical models
Depositing User:	Ms. Suzila Mohamad Kasim
Last Modified:	24 Apr 2025 06:28
URI:	http://webagris.upm.edu.my/id/eprint/24181

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