Top

Journal of Intelligent Information Systems

Published in:

01-10-2014

A rule-based expert system for earthquake prediction

Authors: Aqdas Ikram, Usman Qamar

Published in: Journal of Intelligent Information Systems | Issue 2/2014

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

next article Learning from syntax generalizations for automatic semantic annotation

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Agrawal, R., & Bayardo, R.J. Jr (1999). Mining the most interesting rules. In Proceedings of the 1999 ACM SIGKDD international conference on knowledge discovery and data mining - SIGKDD ’99 (pp. 145–154).

Allen, C.R., Edwards, W., Hall, W.J., Knopoff, L., Raleigh, C.B., Savit, C.H., Toksoz, M.N., Turner, R.H. (1976). Predicting earthquakes: a scientific and technical evaluation—with implications for society. Panel on Earthquake Prediction of the Committee on Seismology, Assembly of Mathematical and Physical Sciences, National Research Council, US National Academy of Sciences. Washington.

Alvan, H.V., & Azad, F.H (2011a). Satellite remote sensing in earthquake prediction. A review. doi:10.1109/NatPC.2011.6136371.

Alvan, H.V., & Azad, F.H. (2011b). Satellite remote sensing in earthquake prediction. A review, National Postgraduate Conference (NPC), Publication Year (pp. 1–5).

Aminzadeh, F., Katz, S., Aki, K (1994). Adaptive neural nets for generation of artificial earthquake precursors. IEEE transactions on geoscience and remote sensing (Vol. 32, no. 6).

ANSS Catalog Search (2013). http://www.ncedc.org/anss/catalog-search.html. Accessed Mar 2013.

Dehbozorgi, L., & Farokhi, F. (2010a). Effective feature selection for short-term earthquake prediction using Neuro-Fuzzy classifier. Centran TehranBranch. Sci. Assoc. of Electr. & Electron. Eng., Islamic Azad University. Tehran.

Dehbozorgi, L., & Farokhi, F. (2010b). Effective feature selection for short-term earthquake prediction using Neuro-Fuzzy classifier. In 2010 2nd IITA international conference on geoscience and remote sensing (IITA-GRS), (Vol. 2, Publication Year 2010, pp. 165–169).

de Souza, F.T., & Wang, Z. (2010). A Data Mining approach to predict mass movements induced by seismic events in Sichuan, China. In 2010 6th international conference on natural computation (ICNC), (Vol. 3, Publication Year 2010, pp. 1172–1177).

Gokhberg, M.B., Morgounov, V.A., Pokhotelov, O.A. (1995). Earthquake prediction, seismo-electromagnetic phenomena. Amsterdam: Gordon and Breach Science Publishers.

Han, J., & Pei, J. (2004). Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8, 53–87.MathSciNetCrossRef

Hanna, A.M., Ural, D., Saygili, G. (2007). Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dynamics and Earthquake Engineering, 27(6).

John, B., Rundle, D.L., Turcotte, W.K. (2000). Geocomplexity and the physics of earthquakes (Geophysical Monograph Series), 1st edn. Wiley-AGU; .

Jusoh, M.H., Ya’acob, N., Saad, H., Sulaiman, A.A., Baba, N.H., Awang, R.A., et al. (2008). Earthquake prediction technique based on GPS dual frequency system in equatorial region. IEEE International RF and Microwave Conference Proceedings, 372–376. doi:10.1109/RFM.2008.4897373.

Kossobokov, V.G., & Soloviev, A.A. (2008). Prediction of extreme events: fundamentals and prerequisites of verification. Russian Journal of Earth Sciences.

Li, X. (2011). An intelligent system for earthquake early warning intelligent systems and applications (ISA). In 2011 3rd international workshop on digital object identifier (Publication Year 2011, pp. 1–4).

Liao, S-H. (2005). Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Systems with Applications, 28(1), 93–103.CrossRef

Liu, J.Y., Chen, Y.I., Chuo, Y.J., Tsai, H.F (2001). Variations of ionospheric total content during the Chi-Chi earthquake. Geophysical Research Letters, 28, 1381–1386.

Liu, S., Yang, D., Ma, B., Wu, L, Li, J., Dong, Y. (2007). On the features and mechanism of satellite infrared anomaly before earthquakes in Taiwan Region. In IEEE international geoscience and remote sensing symposium, 2007. IGARSS 2007 (Publication Year 2007, pp. 3719–3722).

Mouri, M., Funase, A., Takumi, I., Cichocki, A., Yasukawa, H., Hata, M. (2008). Effectiveness of global signal elimination from environmental electromagnetic signals for earthquake prediction. In Information theory and its applications, 2008. ISITA 2008. International symposium on publication year 2008 (pp. 1–6).

Nimmagadda, S.L., & Dreher, H. (2007a). Ontology based data warehouse modeling and mining of earthquake data: prediction analysis along Eurasian-Australian continental plates. In 2007 5th IEEE international conference on industrial informatics (Vol. 1, pp. 597–602).

Nimmagadda, S.L., & Dreher, H. (2007b). Ontology based data warehouse modeling and mining of earthquake data: prediction analysis along Eurasian-Australian continental plates. In 2007 5th IEEE international conference on industrial informatics (Vol. 1, Publication Year 2007, pp. 597–602).

Otari, G.V., & et al (2012). A review of application of data mining in earthquake prediction. International Journal of Computer Science and Information Technologies, 3(2).

Parker, B. (2010). The power of the sea: tsunamis, storm surges, rogue waves, and our quest to predict disasters (pp. 7–30). Palgrave Macmillan.

Pulinets, S. (2004). Ionospheric precursors of earthquakes; Recent advances in theory and practical applications. TAO, 15(3), 413–435.

Qiang, Z., Zeng, Z., Wang, J., Ma, A., Hongjie, X. (2009). Study on short-term and imminent earthquake prediction using the satellite thermal infrared technique. In 4th international conference on cooperation and promotion of information resources in science and technology, 2009. COINFO ’09 (Publication Year 2009, pp. 372–381).

Sikder, I.U., & Munakata, T. (2009). Application of rough set and decision tree for characterization of premonitory factors of low seismic activity. Expert Systems with Applications, 36(1).

Tsunekawa, H. (1998). A fuzzy neural network prediction model of the principal motions of earthquakes based on preliminary tremors. In Proceedings of the 24th annual conference of the IEEE industrial electronics society, 1998. IECON ’98 (Vol. 1, Publication Year 1998, pp. 46–51).

USGS (2013). Earthquake hazards program. http://earthquake.usgs.gov/. Accessed Mar 2013.

Wei, X., Cui, X., Jiang, C., Zhou, X (2009). The earthquake probability prediction based on weighted factor coefficients of principal components. IEEE. doi:10.1109/ICNC.2009.438.

Wu, S., Fang, M., Li, Y., Zhang, B. (2008). An earthquake sequential pattern mining algorithm based on general constraint young computer scientists. In The 9th international conference 2008. ICYCS 2008 (Publication Year 2008, pp. 1878–1883).

Wu, L., Qin, K., Liu, S. (2012). GEOSS-based thermal parameters analysis for earthquake anomaly recognition. Proceedings of the IEEE, 100(10), 2891–2907.CrossRef

Xing, Z., Pei, J., Dong, G., Yu, P.S (2008). Mining sequence classifiers for early prediction.

Title: A rule-based expert system for earthquake prediction
Authors: Aqdas Ikram
Usman Qamar
Publication date: 01-10-2014
Publisher: Springer US
Published in: Journal of Intelligent Information Systems / Issue 2/2014
Print ISSN: 0925-9902
Electronic ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-014-0316-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2014

Time-slide window join over data streams

Image understanding and the web: a state-of-the-art review

Learning from syntax generalizations for automatic semantic annotation

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

Improving process models by mining mappings of low-level events to high-level activities

Consistent feature selection and its application to face recognition

Premium Partner