International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.10, No.2, Feb. 2018

Rapid Earthquake Alarm System and Real-Time Automated Action: Application of Multi-Agent Hardware

Full Text (PDF, 752KB), PP.73-82

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Ahmad Ghodselahi, Mostafa Ghodselahi, Farid Tondnevis

Index Terms

Multi-agent Hardware Precursor;earthquake Rapid Reaction


Earthquake is the most dangerous natural disaster in the whole era of human being life. Scientist efforts for predicting earthquake have no prolific result, so far. The earth complexity and geology structures are the main obstacles of these efforts. The importance of time at the occurrence of the earthquake has resulted in using powerful systems for real-time alarming and therefore lessening the casualties of the earthquake. In this paper we have designed a rapid earthquake alarm system and we have implemented it in parallel processing and continuous processing.  We have tried to apply hardware intelligent agents for real-time and parallel processing of data and data fusion of sensors. By applying this technology, the performance of rapid earthquake alarm system will be improved. Through this improvement, the rapid and automated action of rapid earthquake alarm system can lead to reducing the effect of earthquake.

Cite This Paper

Ahmad Ghodselahi, Mostafa Ghodselahi, Farid Tondnevis, "Rapid Earthquake Alarm System and Real-Time Automated Action: Application of Multi-Agent Hardware", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.2, pp.73-82, 2018. DOI: 10.5815/ijitcs.2018.02.07


[1]Yeats, R. S. and A. M. Robert .2001. Geology of Earthquakes. Encyclopedia of Physical Science and Technology. New York, Academic Press: 649-661.

[2]U.S. Geological Survey .2010. Faqs - Measuring Earthquakes. Retrieved 2010/02/18, 2010, from

[3]Faccioli, E. 1983. Measures of Strong Ground Motion Derived from a Stochastic Source Model. International Journal of Soil Dynamics and Earthquake Engineering 2(3): 135-149.

[4]Papadimitriou, E. E. 1994. Long-Term Earthquake Prediction of Large Shallow Mainshocks Along the Tonga-Kermadec-New Zealand Seismic Zone Based on a Time- and Magnitude-Predictable Model. Tectonophysics 235(4): 347-360.

[5]Shalimov, S. and M. Gokhberg .1998. Lithosphere-Ionosphere Coupling Mechanism and Its Application to the Earthquake in Iran on June 20, 1990. A Review of Ionospheric Measurements and Basic Assumptions. Physics of the Earth and Planetary Interiors 105(3-4): 211-218.

[6]Saygili, G. and E. M. Rathje .2009. Probabilistically Based Seismic Landslide Hazard Maps: An Application in Southern California. Engineering Geology 109(3-4): 183-194.

[7]Meng, J. 2007. Earthquake Ground Motion Simulation with Frequency-Dependent Soil Properties. Soil Dynamics and Earthquake Engineering 27(3): 234-241.

[8]Utkin, V. I. and A. K. Yurkov. 2010.Radon as a Tracer of Tectonic Movements. Russian Geology and Geophysics 51(2): 220-227.

[9]KülahcI, F., M. Inceِz, M. Dogru, E. Aksoy and O. Baykara .2009. Artificial Neural Network Model for Earthquake Prediction with Radon Monitoring. Applied Radiation and Isotopes 67(1): 212-219.

[10]Uyeda, S., T. Nagao and M. Kamogawa .2009. Short-Term Earthquake Prediction: Current Status of Seismo-Electromagnetics. Tectonophysics 470(3-4): 205-213.

[11]Novikova O and R. I 1999. Performance of the Earthquake Prediction Algorithm Cn in 22 Regions of the World. Physics of the Earth and Planetary Interiors 111: 207-213.

[12]Vorobieva, I. A. 1999. Prediction of a Subsequent Large Earthquake. Physics of the Earth and Planetary Interiors 111(3-4): 197-206.

[13]Murru, M., R. Console and G. Falcone. (2009). Real Time Earthquake Forecasting in Italy. Tectonophysics 470(3-4): 214-223.

[14]Pulinets, S. A. 2006. Space Technologies for Short-Term Earthquake Warning. Advances in Space Research 37(4): 643-652.

[15]Talebian, M.,Latifi, K., Amini, B., Fereydoni, A., Anbaran, H.and Nemati, M., (2007). Investigating the records of electromagnetic precursor station of Tehran for a two-year period. The first conference of earthquake precursor. 160-166.

[16]Resaneh, G and Hajbabaee, N. (2007). Introducing some weather precursor and analyzing the trend of their variations prior to earthquake (The case of Bam earthquake). The first conference of earthquake precursor. 102-109.

[17]Ondoh, T. 2009. Investigation of Precursory Phenomena in the Ionosphere, Atmosphere and Groundwater before Large Earthquakes of M > 6.5. Advances in Space Research 43(2): 214-223.

[18]Nanjo, K. Z., J. R. Holliday, C. c. Chen, J. B. Rundle and D. L. Turcotte .2006. Application of a Modified Pattern Informatics Method to Forecasting the Locations of Future Large Earthquakes in the Central Japan. Tectonophysics 424(3-4): 351-366.

[19]Adeli, H. and A. Panakkat .2009. A Probabilistic Neural Network for Earthquake Magnitude Prediction. Neural Networks 22(7): 1018-1024.

[20]Alcik, H., O. Ozel, Y. M. Wu, N. M. Ozel and M. Erdik. 2010. An Alternative Approach for the Istanbul Earthquake Early Warning System. Soil Dynamics and Earthquake Engineering. In Press, Corrected Proof.

[21]Manjunatha, K.C., Mohana, H.S. and Vijaya, P.A.2015. Implementation of computer vision based industrial fire safety automation by using neuro-fuzzy algorithm. I.J. nformation technology and computer science04:14-27.

[22]Hepsiba, M. and Justus, S. 2016. A review on the knowledge representation models and its implementations. I.J. Information technology and computer science 10:72-81.

[23]Hazra, T., Kumar C. and Nene, J. 2017 Strategies for searching targets using mobile sensors in defense scenarios. I.J. Information technology and computer science 5:61-70.

[24]Abedinzadeh, S. and Sadaoui, S. 2013. A Rough Sets-based Agent Trust Management Framework. I.J. Intelligent systems and applications 04: 1-19.

[25]Balbo, F. and S. Pinson .2005. Dynamic Modeling of a Disturbance in a Multi-Agent System for Traffic Regulation. Decision Support Systems 41(1): 131-146.

[26]Chow, H. K. H., K. L. Choy and W. B. Lee. 2007. A Dynamic Logistics Process Knowledge-Based System - an Rfid Multi-Agent Approach. Knowledge-Based Systems 20(4): 357-372.

[27]Naji, H. R. 2008. Solving Complex Computational Problems Using Multiagents Implemented in Hardware. Computing in Science & Engineering 10(5): 54-63.

[28]Naji, H. R., B. E. Wells and L. Etzkorn .2004. Creating an Adaptive Embedded System by Applying Multi Agent Techniques to Reconfigurable Hardware. Future Generation Computer Systems 20(6): 1055-1081.

[29]Doniec, A., R. Mandiau, S. Piechowiak and S. Espié .2008. A Behavioral Multi-Agent Model for Road Traffic Simulation. Engineering Applications of Artificial Intelligence 21(8): 1443-1454.

[30]Naji, H. R. and B. E. Wells .2002. On Incorporating Multi-Agents in Combined Hardware/Software Based Reconfigurable Systems - a General Architectural Framework. System Theory, 2002. Proceedings of the Thirty-Fourth Southeastern Symposium on.

[31]Biswas, P. K. and S. Phoha .2004. A Middleware-Driven Architecture for Information Dissemination in Distributed Sensor Networks. Intelligent Sensors, Sensor Networks and Information Processing Conference Proceedings of the 2004.

[32]Naji, H. R. 2005. Sensor Fusion Using Hardware Agents, an Implementation Example. Industrial Informatics, 2005. INDIN '05. 2005 3rd IEEE International Conference on.

[33]Hsiao, N.-C., Y.-M. Wu, L. Zhao, D.-Y. Chen, W.-T. Huang, K.-H. Kuo, T.-C. Shin and P.-L. Leu.2010 A New Prototype System for Earthquake Early Warning in Taiwan. Soil Dynamics and Earthquake Engineering In Press, Corrected Proof.

[34]Asgary, A., J. K. Levy and N. Mehregan.2007. Estimating Willingness to Pay for a Hypothetical Earthquake Early Warning Systems. Environmental Hazards 7(4): 312-320.

[35]Naji, H. R. 2003. Implementing Data Flow Operations with Multi Hardware Agent Systems. System Theory, 2003. Proceedings of the 35th Southeastern Symposium on.