Study of expert systems in predicting earthquake


Associate Professor Department of the Computer Engineering, Faculty of Engineering,University of Guilan, Rasht, Iran.


The crisis in the event of an incident or accident occurs suddenly and unexpectedly that urgent attention is needed for proper decision making. Despite technological advances, the suffering caused by natural disaster such as earthquake, flood, avalanche, hurricane, volcano, fire and there is abnormal. Mining activities are always risks with the risks of mine called, has been associated. Seismic hazard in underground mines is one of the threats to human life. Seismic hazard identification much more difficult to identify the natural hazards to an earthquake. Using advanced seismic earthquake and Seismoacoustic predict. The accuracy of the information generated is not optimal. The complexity of seismic processes and the disproportion between the low-energy seismic events and a number of high-energy phenomena (eg greater than 10 ^ 4 J) makes statistical techniques to predict the seismic hazard is not enough. So the search for better opportunities for risk prediction has become imperative. Clustering techniques for seismic hazard data and artificial neural networks were used to predict. In most applications, the results obtained by the methods listed in the situation "dangerous" and "safe" have been reported. Unbalanced distribution of positive samples (dangerous situation) and negative (safe condition), is a serious problem in seismic hazard forecasting. Currently, the methods used can not be expected to take appropriate and high sensitivity. A number of factors related to the risk of earthquakes, was proposed. Among other factors, the shake with energy greater than 10 ^ 4 J mentioned earthquake prediction can be defined with different methods. But the main goal is for all methods of seismic hazard assessment. In some cases, the data on the time and date, an increase in seismic activity that could lead to the destruction of rocks using pressure (Rockburst), the predicted.
You can not focus on one parameter, occurrence or non-occurrence of earthquakes in the area or within the time limit specified. Should study several parameters at the same time be an acceptable technique for predicting earthquake found that one way to do this is to use expert system. The news systems provided that these systems have the ability to use more, better and smarter them. For example, in hydrology analysis only considered changes in water ions other parameters such as fault behavior, changes in sea level, etc., are not considered to predict earthquakes. On the other hand all expert systems to predict earthquakes from Precursor not use to predict. Some expert systems by the time information, location and depth of previous earthquakes, earthquakes predict the future. Studies have shown that large earthquakes decision tree and artificial neural network down accurately predicted. In this article by support vector machines based on particle swarm big earthquakes with higher accuracy than was some other expert systems.
Expert systems commonly used in earthquake prediction. In these expert systems, various parameters are used such as fault behavior, the concentration of radon, energy, pulse and the number of bumps. The occurrence of earthquakes can be measured by checking these parameters. The accuracy of earthquake prediction by expert systems is relatively higher than non-expert system methods. In this paper, the accuracy and the kinds of data used in different expert systems in the field of earthquake prediction were studied. In addition, different algorithms to predict earthquake with an expert system based on support vector machines, decision trees, neural networks, support vector machines based on particle swarm optimization, Bayesian and MLP network was implemented in Rapidminer. The results show that support vector machine-based expert system that is optimized by particle swarm algorithm in comparison to neural network-based expert systems, support vector machines, Bayesian, decision tree and network MLP has a better prediction accuracy.


Main Subjects

Abraham, A., 2005, Rule-based expert systems, handbook of measuring system design, 909-919.
Abrahamson, N. and Silva, W., 2008, Summary of the Abrahamson and Silva NGA ground-motion relations, Earthquake Spectra, 24(1), 67-97.
Ahumada, A., Altunkaynak, A. and Ayoub, A., 2015, Fuzzy logic-based attenuation relationships of strong motion earthquake records, Expert Systems with Applications, 42(3), 1287-1297.
Aminzadeh, F., Katz, S. and Aki, K., 1994, Adaptive neural nets for generation of artificial earthquake precursors, IEEE Transactions on Geoscience and Remote Sensing, 32(6), 1139-1143.
Basak, D., Pal, S. and Patranabis, D. C., 2007, Support vector regression, Neural Inf. Process, 11, 203-225.
Bofeng, Z. and Yue, L. 2005, Customized explanation in Expert System for earthquake prediction, 17th IEEE International Conference on Tools with Artificial Intelligence, 371-376, Hong Kong, China.
Borghi, A., Aoudia, A., Riva, R. E. M. and Barzaghi, R., 2009, GPS monitoring and earthquake prediction: a success story towards a useful integration, Tectonophysics, 465(1-4), 177-189.
Cherkassky, V. and Ma, Y., 2004, Practical of SVM parameters and noise estimation for SVM regression, Neural Networks, 17, 113-126.
Dehbozorgi, L. and Farokhi, F., 2010, Effective feature selection for short-term earthquake prediction using neuro-fuzzy classifier, Second IITA International Conference on Geoscience and Remote Sensing, 2, 165-169.
Durkin, J., 1994, Expert systems, design and development, Prentice Hall, Englewood Cliffs, NJ.
Dutta, P. K., Mishra, Q. P. and Naskar, M. K., 2013, A review of operational earthquake forecasting methodologies using linguistic fuzzy rule-based models from imprecise data with weighted regression approach, Journal of Sustainability Science and Management, 8(2), 220-235.
Dutta, P. K., Mishra, O. P. and Naskar, M. K., 2012, Decision analysis for earthquake prediction methodologies: fuzzy inference algorithm for trust validation, International Journal of Computer Applications, 45(4), 13-20.
Hadjimichael, M., Kuciauskas, A. P., Tag, P. M., Bankert, R. L. and Peak, J. E., 2002, A meteorological fuzzy expert system incorporating subjective user input, Knowledge and Information System, 4(3), 350-369.
Ikram, A. and Qamar, U., 2014, A rule-based expert system for earthquake prediction, Journal of Intelligent Information Systems, 43(2), 205-230.
Ikram, A. and Qamar, U., 2015, Developing and expert system based on association rules and predicate logic for earthquake prediction, Knowledge-Based Systems, 75, 87-103.
Jalal Kamali, H., Bidokhti, A. A. and Amiri, H., 2009, Relation between integral effect of sub-surface temperature variation (І) seismic effects, Nat. Hazards Earth Syst. Sci., 9, 1815-1821.
Kabiesz, J., Sikora, B., Sikora, M. and Wrobell, L., 2013, Application of rule-based models for seismic hazard prediction in coal mines, Acta Montanistica Slovaca, 18(4), 262-277.
Kawabe, I., Ohno, I. and Nadano, S., 1988, Groundwater flow records indicating earthquake occurrence and induced Earth's free oscillation, Geophysical Research Letters, 15(11), 1235-1238.
King, C. Y., Azuma, S., Ohno, M., Asai, Y., He, P., Kitagawa, Y., Igarashi, G. and Wakita, H., 2000, In search of earthquake precursors in the water-level data of 16 closely clustered wells at Tono, Japan, Geophysical Journal International, 143, 469-477.
Lakshmi, K. R., Nagesh, Y. and Krishna, M. V., 2014, Analysis on predicting earthquakes through an abnormal behaviour of animals, International Journal of Scientific & Engineering Research, 5(4), 845-857.
Meyer, D. and Wien, F.T., 2012, Support vector machines, The Interface to libsvm in package e1071, 1-8.
Moustra, M., Avraamides, M. and Christodoulou, C., 2011, Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals, Expert Systems with Applications, 38(12), 15032-15039.
Reyes, J., Esteban, A. M. and Alvarez, F. M., 2013, Neural networks to predict earthquakes in Chile, Applied Soft Computing, 13(2), 1314-1328.
Shahrabi, M., 2014, Creation of an expert system to estimate the product sale based on fuzzy logic, International Journal of Modern Computer Science and Engineering, 3(1), 1-8.
Sikder, I. U. and 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), 102-110.
Tertyshnikov, A. V., Skripachev, V. O. and Chemyavskii, G. V., 2009, Variations in deceleration of space vehicles in the upper ionosphere before strong earthquake, Doklady Earth Sciences, 424(1), 180-184.
Tezcan, J. and Cheng, Q., 2012, Support vector regression for estimating earthquake response spectra, Bulletine of Earthquake Engineering, 10(4), 1205-1219.
Tonooka, H., Palluconi, F. D., Hook, S. J. and Matsunaga, T., 2005, Vicarious calibration of ASTER thermal infrared bands, IEEE Transaction on Geoscience and Remote Sensing, 43(12), 2733-2746.
Torkar, D., Zmazek, B., Vaupotic, J. and Kobal, L., 2010, Application of artificial neural networks in simulating radon levels in soil gas, Chemical Geology, 270(1-4), 1-8.
Tronin, A. A., 2010, Satellite remote sensing in seismology, A review, Remote Sensing, 2(1), 124-150.
UCI., 2015, Machine learning respository: Https://
Vapnik, V., 1995, The nature of statistical learning theory, Springer Verlag
Wang, S., 1997, Neural networks in generalizing expert knowledge, Computers and Industrial Engineering, 32(1), 67-76.
Wright, T. J., 2002, Remote monitoring of the earthquake cycle using satellite radar interferometry, Philosophical Transaction of the Royal Society A, Mathematical, Physical and Engineering Sciences, 360(1801), 2873-2888.
Zoback, M. L., Geist, E., Pallister, J., Hill, D. P., Young, S. and McCausland, W., 2013, Advances in natural hazard science and assessment, 1963-2013, the impact of the geological sciences on society, Geological Society of America Special Paper, 501, 81-154.