Please use this identifier to cite or link to this item: http://umt-ir.umt.edu.my:8080/handle/123456789/7004
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dc.contributor.authorP. K. DUTTA-
dc.contributor.authorO. P. MISHRA-
dc.contributor.authorM. K. NASKAR-
dc.date.accessioned2017-10-04T04:08:32Z-
dc.date.available2017-10-04T04:08:32Z-
dc.date.issued2013-
dc.identifier.issn18238556-
dc.identifier.urihttp://hdl.handle.net/123456789/7004-
dc.description.abstractIt 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 fnding 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 fnding 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.en_US
dc.language.isoenen_US
dc.publisherJournal of Sustainability Science and Managementen_US
dc.subjectEarthquakesen_US
dc.subjectprecursorsen_US
dc.subjectsoft computingen_US
dc.subjectpredictionen_US
dc.subjectalgorithmsen_US
dc.subjectfuzzy analysisen_US
dc.subjectregressionen_US
dc.subjectbest-ften_US
dc.subjectrule inductionen_US
dc.titleA REVIEW OF OPERATIONAL EARTHQUAKE FORECASTING METHODOLOGIES USING LINGUISTIC FUZZY RULE-BASED MODELS FROM IMPRECISE DATA WITH WEIGHTED REGRESSION APPROACHen_US
dc.typeArticleen_US
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