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Journal of Intelligent Information Systems

Erschienen in:

01.10.2014

A rule-based expert system for earthquake prediction

verfasst von: Aqdas Ikram, Usman Qamar

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2014

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Abstract

Earthquake is a natural disaster which causes extensive damage as well as the death of thousands of people. Earthquake professionals for many decades have recognized the benefits to society from reliable earthquake predictions. Techniques like: mathematical modelling, hydrology analysis, ionosphere analysis and even animal responses have been used to forecast a quake. Most of these techniques rely on certain precursors like, stress or seismic activity. Data mining techniques can also be used for prediction of this natural hazard. Data mining consists of evolving set of techniques such as association rule mining that can be used to extract valuable information and knowledge from massive volumes of data. The aim of this study is to predict a subsequent earthquake from the data of the previous earthquake. This is achieved by applying association rule mining on earthquake data from 1979 to 2012. These associations are polished using predicate-logic techniques to draw stimulating production-rules to be used with a rule-based expert system. Prediction process is done by an expert system, which takes only current earthquake attributes to predict a subsequent earthquake. The rules generated for predicting the earthquake are mathematically validated as well as tested on real life earthquake data. Results from our study show that the proposed rule-based expert system is able to detect 100 % of earthquakes which actually occurred within 15 hours at-most within a defined range, depth and location. This work solely relies on previous earthquake data for predicting the next.

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Titel: A rule-based expert system for earthquake prediction
verfasst von: Aqdas Ikram
Usman Qamar
Publikationsdatum: 01.10.2014
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2014
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-014-0316-5

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