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A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

DOI: 10.4236/cs.2016.711294, PP. 3456-3468

Keywords: Artificial Neural Networks, Back Propagation, Multilayer Neural Network, Earthquakes, Prediction Systems

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Abstract:

The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the earthquakes of magnitude between 4.0 and 6.0.

 References

[1]  Narayanakumar, S., Raja, K., Dhanasekaran, R. and Indradevi, M. (2014) A Review of Application of Intelligent Techniques in Earthquake Prediction. IJEEEAR, 2, 216-219.
[2]  Adeli, H. and Hung, S.L. (1995) Machine Learning: Neural Networks, Genetic Algoritms, and Fuzzy Systems. Wiley, New York.
[3]  Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. 2nd Edition, Prentice- Hall, Englewood Cliffs, NJ.
[4]  Adeli, H. and Panakkat, A. (2009) A Probabilistic Neural Network for Earthquake Magnitude Prediction. Elsevier Neural Networks, 22, 1018-1024.
http://dx.doi.org/10.1016/j.neunet.2009.05.003
[5]  Sawant, S.S. and Topannavar, P.S. (2015) Introduction to Probabilistic Neural Network—Used for Image Classifications. International Journal of Advanced Research in Computer Science & Software Engineering, 5.
[6]  Marco-Detchart, C., Cerron, J., De Miguel, L., Lopez-Molina, C., Bustince, H. and Galar, M. (2016) A Framework for Radial Data Comparison and Its Application to Fingerprint Analysis. Applied Soft Computing, 46, 246-259.
http://dx.doi.org/10.1016/j.asoc.2016.05.003
[7]  Malhotra, R. (2016) An Empirical Framework for Defect Prediction Using Machine Learning Techniques with Android Software. Applied Soft Computing, In Press.
http://dx.doi.org/10.1016/j.asoc.2016.04.032
[8]  Raghavendra, U., Rajendra Acharya, U., Fujita, H., Gudigar, A., Tan, J.H. and Chokkadi, S. (2016) Application of Gabor Wavelet and Locality Sensitive Discriminant Analysis for Automated Identification of Breast Cancer Using Digitized Mammogram Images. Applied Soft Computing, 46, 151-161.
http://dx.doi.org/10.1016/j.asoc.2016.04.036
[9]  Vats, E. and Chan, C.S. (2016) Early Detection of Human Actions—A Hybrid Approach. Applied Soft Computing, 46, 953-966.
http://dx.doi.org/10.1016/j.asoc.2015.11.007
[10]  Bhaumik, H., Bhattacharyya, S., Nath, M.D. and Chakrabory, S. (2016) Hybrid Soft Computing Approaches to Content Based Video Retrieval: A Brief Review. Applied Soft Computing, 46, 1008-1029.
http://dx.doi.org/10.1016/j.asoc.2016.03.022
[11]  Chagas, S.H., Martins, J.B. and de Oliveria, L.L. (2012) An Approach to Localization Scheme of wireless Sensor Networks Based on Artificial Neural Networks and Genetic Algorithms. 2012 IEEE 10th International New Circuits and Systems Conference (NEWCAS), 17-20 June 2012, 137-140.
http://dx.doi.org/10.1109/NEWCAS.2012.6328975
[12]  Wang, H.G., Li, G.L., Ma, Z.H. and Li, X.L. (2012) Application of Neural Networks to Image Recognition of Plant Diseases. 2012 International Conference on Systems and Informatics (ICSAI), Yantai, 19-20 May 2012, 2159-2164.
http://dx.doi.org/10.1109/ICSAI.2012.6223479
[13]  Saroha, S. and Aggarwal, S.K. (2014) Multi Step Ahead Forecasting of Wind Power by Different Class of Neural Networks. 2014 Recent Advances in Engineering and Computational Sciences (RAECS), Chandigarh, 6-8 March 2014, 1-6.
http://dx.doi.org/10.1109/ICSAI.2012.6223479
[14]  Dhaliwal, B.S. and Pattnaik, S.S. (2012) Performance Evaluation of Artificial Neural Networks in Microstrip Fractal Antenna Parameter Estimation. 2012 IEEE International Conference on Communication Systems (ICCS), Singapore, 21-23 November 2012, 135-139.
http://dx.doi.org/10.1109/ICCS.2012.6406124
[15]  Ni, Q.K., Guo, C. and Yang, J. (2012) Research of Face Image Recognition Based on Probabilistic Neural Networks. 2012 24th Chinese Control and Decision Conference (CCDC), Taiyuan, 23-25 May 2012, 3885-3888.
http://dx.doi.org/10.1109/CCDC.2012.6243102
[16]  Xu, X., Shan, D., Wang, G. and Jiang, X. (2016) Multimodal Medical Image Fusion Using PCNN Optimized by the QPSO Algorithm. Applied Soft Computing, 46, 588-595.
http://dx.doi.org/10.1016/j.asoc.2016.03.028
[17]  Mosavi, M.R. (2007) GPS Receivers Timing Data Processing Using Neural Networks: Optimal Estimation and Errors Modeling. International Journal of Neural Systems, 17, 383- 393.
http://dx.doi.org/10.1142/S0129065707001226
[18]  Adeli, H. (2001) Neural Networks in Civil Engineering: 1989-2000. Computer-Aided Civil and Infrastructure Engineering, 16, 126-142.
http://dx.doi.org/10.1111/0885-9507.00219
[19]  Christodoulou, M.A. and Kontogeorgou, C. (2008) Collision Avoidance in Commercial Aircraft Free Flight, via Neural Networks and Non-Linear Programming. International Journal of Neural Systems, 18, 371-387.
http://dx.doi.org/10.1142/S0129065708001658
[20]  Adeli, H. and Panakkat, A. (2009) A Probabilistic Neural Network for Earthquake Magnitude Prediction. Neural Networks, 22, 1018-1024.
http://dx.doi.org/10.1016/j.neunet.2009.05.003
[21]  Astuti, W., Akmeliawatim, R., Sediono, W. and Salami, M.J.E. (2014) Hybrid Technique Using Singular Value Decomposition (SVD) and Support Vector Machine (SVM) Approach for Earthquake Prediction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 1719-1728.
http://dx.doi.org/10.1109/JSTARS.2014.2321972
[22]  Dutta, P.K., Mishra, O.P. and Naskar, M.K. (2013) Evaluation of Seismogenesis Behavior in Himalayan Belt Using Data Mining Tools for Forecasting. Central European Journal of Geosciences, 5, 236-253.
http://dx.doi.org/10.2478/s13533-012-0127-6
[23]  Kulahci, F., Inceoz, M., Dogru, M., Aksoy, E. and Baykara, O. (2009) Artificial Neural Network Model for Earthquake Prediction with Radon Monitoring. Applied Radiation and Isotopes, 67, 212-219.
http://dx.doi.org/10.1016/j.apradiso.2008.08.003
[24]  Phili, G., Bhakuni, S.S., Suresh, N. and Virdi, N.S. (2014) Late Pleistocene Faulting along the Growing Janauri Anticline and Seismic Potential in the North-Western Frontal Himalaya, India. Himalayan Geology, 35, 89-96.
[25]  Pandey, P. and Pandey, A.K. (2004) Soft Sediment Deformation Features in the Meizoseismal Region of 1999—Chamoli earthquake of Garhwal Himalaya and Their Significance. Himalayan Geology, 25, 79-87.
[26]  Gutenberg, B. and Richter, C.F. (1956) Earthquake Magnitude, Intensity, Energy and Acceleration. Bulletin of the Seismological Society of America, 46, 105-146.
[27]  Gutenberg, B. and Richter, C.F. (1944) Frequency of Earthquakes in California. Bulletin of the Seismological Society of America, 34, 185-188.
[28]  Bayrak, Y., Yilmazturk, A. and Ozturk, S. (2002) Lateral Variation of the Modal (a/b) Values for the Different Regions of the World. Journal of Geodynamics, 34, 653-666.
http://dx.doi.org/10.1016/S0264-3707(02)00037-6
[29]  Volant, P., Grasso, J.-R., Chatelain, J.-L. and Frogneux, M. (1992) B-Value, Aseismic Deformation and Brittle Failure within an Isolated Geological Object: Evidences from a Dome Structure Loaded by Fluid Extraction. Geophysical Research Letters, 19, 1149-1152.
http://dx.doi.org/10.1029/92GL01074
[30]  Wiemer, S. (2001) A Software Package to Analyze Seismicity: ZMAP. Seismological Research Letters, 72, 373-382.
http://dx.doi.org/10.1785/gssrl.72.3.373
[31]  AL-Heety, E.A.M. (2011) Variation of b-Value in the Earthquake Frequency-Magnitude Distribution with Depth in the Intraplate Regions. International Journal of Basic & Applied Sciences, 11, 29-37.
[32]  Tsompanakis, Y., Lagaros, N.D. and Stavroulakis, G.E. (2008) Soft Computing Techniques in Parameter Identification and Probabilistic Seismic Analysis of Structures. Science Direct, Advances in Engineering Software, 39, 612-624.
http://dx.doi.org/10.1016/j.advengsoft.2007.06.004

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