Soft Computing

, Volume 21, Issue 19, pp 5805–5813 | Cite as

BAT-ANN based earthquake prediction for Pakistan region

  • Sehrish Saba
  • Faraz Ahsan
  • Sajjad Mohsin
Methodologies and Application


Earthquakes are natural disasters which may result in heavy losses. Accurate prediction of the time and intensity of future earthquakes can lead to minimizing losses due to earthquakes. A number of earthquake predictions have been proposed based on mathematical and statistical models. In this paper, we present an earthquake prediction technique using Bat Algorithm (BA) and Feed Forward Neural Network (FFNN). The BA is used to train the weights of the FFNN to predict future earthquakes on the basis of past input data. Experimental results show that our proposed approach is highly comparable and more stable than Back Propagation Neural Network (BPNN) with respect to accuracy.


Earthquake prediction Bat Algorithm Artificial neural network Optimization technique 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Statement of human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Adeli H, Panakkat A (2009) A probabilistic neural network for earthquake magnitude prediction. J Neural Netw 22(7)Google Scholar
  2. Akhoondzadeh M (2014) Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran, (Mw = 7.7) earthquake of 16 April 2013. Adv Space Res 53:647–655CrossRefGoogle Scholar
  3. Alarifi ASN, Alarifi NSN, Al-Humidan S (2012) Earthquakes magnitude predication using artificial neural network in northern Red Sea area. J King Saud Univ Sci 24:301–313CrossRefGoogle Scholar
  4. Alavi AH, Gandomi AH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comp Struct 89:2176–2194CrossRefGoogle Scholar
  5. Bodri B (2001) A neural-network model for earthquake occurrence. J Geodyn 32:289–310CrossRefGoogle Scholar
  6. Deep K, Yadav A, Kumar S (2012) Improving local and regional earthquake locations using an advance inversion technique: particle swarm optimization. World J Model Simul 8(2):135–141Google Scholar
  7. Deep K, Yadav A, Kumar S (2011) Determining earthquake locations in NW Himalayan region: an application of particle swarm optimization. Int J Comput Sci Math 3(2):173–181 (ISSN 0974–3189)Google Scholar
  8. Dehbozorgi L, Farokhi F (2010) Effective feature selection for short-term earthquake prediction using neuro-fuzzy classifier. In: 2010 Second IITA International Conference on Geoscience and Remote SensingGoogle Scholar
  9. Khan K, Sahai A (2012) A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. IJISA 4(7):23–29CrossRefGoogle Scholar
  10. Liu Y, Liu H, Zhang B, Wu G (2004) Extraction of if-then rules from trained neural network and its application to earthquake prediction. In: Proceedings of the Third IEEE International Conference on Cognitive Informatics (ICCI’04)Google Scholar
  11. Moustra M, Avraamides M, Christodoulou C (2011) Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals. Exp Syst Appl 38:15032–15039Google Scholar
  12. Prakash D (2012) Bespoke artificial Bee Colony Algorithm to determine the earthquake locations. Adv Mech Eng its Appl (AMEA) 2(3):207 (ISSN 2167–6380)Google Scholar
  13. Preethi G, Santhi B (2011) Study on techniques of earthquake prediction. Int J Comp Appl 29(4) (0975–8887)Google Scholar
  14. Shah H, Ghazali R, Nawi NM (2011) Using artificial bee colony algorithm for MLP training on earthquake time series data prediction. arXiv:1112.4628
  15. SU YP, ZHU QJ (2009) Application of ANN to prediction of earthquake influence. In: Second International Conference on Information and Computing ScienceGoogle Scholar
  16. Suratgar AA, Setoudeh F, Salemi AH (2008) Magnitude of earthquake prediction using neural network. In: Fourth International Conference on Natural Computation IEEE. doi: 10.1109/ICNC.2008.781
  17. US geological survey hazards program website. 2013-6-10Google Scholar
  18. Yang XS (2010) A new meta-heuristic bat-inspired algorithm. In: Gonzalez JR et al. (eds) Nature Inspired Co-operative Strategies for Optimization (NISCO 2010), Studies in Computational Intelligence, Springer, Berlin, vol 284, pp 65–74Google Scholar
  19. Ying W, Yi C, Jinkui Z (2009) The application of RBF neural network in earthquake prediction. In: 2009 Third International Conference on Genetic and Evolutionary Computing. doi: 10.1109/WGEC.2009.81
  20. Zamani AS, Al-Arifi NS, Khan S (2012) Response prediction of earthquake motion using artificial neural networks. IJAR-CSITGoogle Scholar
  21. Zhang Q, Wang C (2008) Using genetic algorithm to optimize artificial neural network: a case study on earthquake prediction. In: Second International Conference on Genetic and Evolutionary Computing. doi: 10.1109/WGEC.2008.96

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Comsats Institute of Information TechnologyIslamabadPakistan
  2. 2.HITEC UniversityTaxilaPakistan

Personalised recommendations