Big Data Analytics using Back Propagation Algorithm for Foretelling Quake

Authors(1) :-K. Kanmani

Quakes are without a doubt most appalling, unavoidable, common cataclysm on Earth. Seismic tremor is a sudden development of the worlds outside layer brought on by the arrival of the anxiety collected along geologic deficiencies by volcanic activity. Earthquake occurrence is one of the significant events in nature that causes both irretrievable financial and physical harm. The Precarious condition emerging because of tremors could be kept away from just by making a solid indicative to anticipate the area, greatness and time of eminent earth quakes. The mechanism of the quake stays to be researched, however a few inconsistencies connected with earthquakes have been found by DEMETER satellite perceptions. It is a helpful and practical approach to utilize the self-versatile counterfeit neural system to develop relations between different manifestation variables and seismic earthquake occurrences. Big data is much more than storage of and access to data. Analytics plays an important role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms. The back-propagation neural network is quite suitable to express the nonlinear relation between earthquake and various anomalies. This paper presents a new approach which can work efficiently with the neural networks on large data sets. Since an Artificial Neural Network is a powerful modeling method, it has been widely used in the earthquake forecasting code. Artificial neural systems are present day machines that have great potential to improve the quality of our life. Advances have been made in applying such systems for problems found difficult for computation.

Authors and Affiliations

K. Kanmani
Department of Computer Science, SRM University, Chennai, India

Earthquake, Big Data, Artificial Neural Networks, Seismic systems and Earth electric field

  1. Ashif Pannakkat and Hojjat Adeli," Neural network models for earthquake magnitude prediction using multiple seismicity indicators", International journal of neural systems, Vol.17,Iss: 1,PP: 13 - 33.
  2. R.Arjun and Ashok kumar, " Artificial neural network - based estimation of peak ground acceleration", ISET journal of Earthquake technology, Paper no.501,Vol.46,No.1 March 2009,PP:19 - 28.
  3. Alarifi A.S. and Alarifi N.S.,"Earthquake magnitude prediction using Artificial neural network in northern red sea area", American Geophysical union, fall meeting 2009.
  4. Fangzhou Xu, Xianfeng Song" Neural Network Model for EarthquakePrediction using DMETER Data and Seismic Belt Information" SecondWRI Global Congress on Intelligent Systems, 2010
  5. Adel Moatti, Mohammad Reza Amin-Nasseri" Pattern Recognition onSeismic Data for Earthquake Prediction Purpose" InternationalConference on Environment, Energy, Ecosystems and Development,2013
  6. Morales-Esteban, F. Martínez Álvarez"Earthquakeprediction inseismogenic areas of the Iberian Peninsula based on computationalintelligence" A. Morales-Esteban et al. / Tectonophysics 593
  7. Feiyan Zhou, Xiaofeng Zhu "Earthquake Prediction Based on LM-BPNeural Network" Proceedings of the 9th International Symposium on Linear Drives for Industry Applications, Volume 1, 2009
  8. Guang-yu Geng, Chuang-hui Li" Research on Seismo-IonosphericAnomalies Using Artificial Neural Network" IEEE,2010.
  9. HUANG Sheng-Zhong" The prediction of the earthquake based onneutral networks", International Conference on Computer Design andApplications (ICCDA), 2010.
  10. Habib Shah, Rozaida Ghazali, and Nazri Mohd Nawi "Using ArtificialBee Colony Algorithm for MLP Training on Earthquake Time SeriesData Prediction", Journal of Computing, 2011.
  11. Tomiyasu "lunar, solar and earthquake projected positions of 138mag. 8.25-5.2 events in california from 1769 to 2004" IEEE,2012.
  12. Reyes, A. Morales-Esteban" Neural networks to predict earthquakes iChile" Reyes et al. / Applied Soft Computing 13, 1314-1328, 2013.
  13. Zhuowei Hu, Lai Wei "Spatial Prediction of Earthquake-InduceSecondary Landslide Disaster in Beichuan County Based on GIS"Research Journal of Applied Sciences, Engineering and Technology 6(20): 3828-3837, 2013.
  14. Niksarlioglu, F. Kulahci "An Artificial Neural Network Model for Earthquake Prediction and Relations between Environmental Parametersand Earthquakes" World Academy of Science, Engineering andTechnology, 2013.
  15. Thanassoulas, "The Earth’s oscillating electric field (T = 1 day) in relation to the occurrence time of large EQs (Ms ≥ 5.0R). A postulated theoretical physical working model and its statistical validation."Cornell university library, pages:10
  16. Shengkai, Wang Chenjmin and Ma li,"Application of Artificial Intelligence in earthquake forecasting",, PP:477 - 481.
  17. Mark A.Kramer, " Non linear principal component analysis using auto associative neural networks by Laboratory for intelligent systems in process engineering", AIChe Journal, Feb 1991, Vol.37,No.2.PP:233-243.
  18. The Earth’s oscillating electric field (T = 1 day) in relation to the occurrence time of large EQs (Ms ≥ 5.0R). A postulated theoretical physical working model and its statistical Mr.Thanassoulas, Mr.C., Klentos, Mr.V., Verveniotis, Mr. G., Zymaris, N.
  19. Shibata and Y. Ikeda, Effect of number of hidden neurons on learning in large-scale layered neural networks,ICROS-SICE International Joint Conference, 2009, p50085013.
  20. Kaikhah and S. Doddameti, Discovering Trends in Large Datasets Using Neural Networks, Applied Intelligence, Springer Science + Business Media, Inc.,
  21. Netherlands, vol. 24, 2006, pp. 51-60. [4] B. Liang and J. Austin, A neural network for mining large volumes of time series data, IEEE Transactions on Neural Networks, 2005, pp.688-693.
  22. Xia, F. Wu, X. Zhang, and Y. Zhuang, Local and global approaches of affinity propagation clustering for large scale data, Journal of Zhejiang University SCIENCE, 2008, p1373-1381.
  23. J. Heaton, Introduction to Neural Networks for C#, Second Edition, Heaton Research, Inc., Chesterfield, St. Louis, United States, Second Edition, 2008, pp. 137-164.

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1031-1038
Manuscript Number : CSEIT1831292
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Kanmani , "Big Data Analytics using Back Propagation Algorithm for Foretelling Quake", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1031-1038, January-February-2018.
Journal URL :

Article Preview