Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Bagus Priambodo, Wayan Firdaus Mahmudy, Muh Arif Rahman


Earthquakes, a type of inevitable natural disaster, is responsible for the highest average death toll per year compared to other types of a natural disaster. Even though it is inevitable, but it can be anticipated to minimize damage and casualties, such as predicting the earthquake‘s magnitude using a neural network. In this study, a backpropagation algorithm is used to train the multilayer neural network to weekly predict the average magnitude of earthquakes in grid-based locations in Indonesia. Based on the findings in this research, the neural network is able to predict the magnitude of earthquakes in grid-based locations across Indonesia with a minimum error rate of 0.094 in 34.475 seconds. This best result is achieved when the neural network is trained for 210 epochs, with 16 neurons used in the input and output layer, one hidden layer consisted of 5 neurons and a learning rate of 0.1. This result showed backpropagation has pretty good generalization capability in order to map the relations between variables when mathematical function is not explicitly available.

Full Text:



D. Guha-Sapir and F. Vos, ―Earthquakes, an Epidemiological Perspective on Patterns and Trends,‖ Advances in Natural and Technological Hazards Research, pp. 13–24, Dec. 2010.

Centre for Research on Epidemiology of Disasters (CRED), ―Press release: EMBARGO 11.00 CET, JANUARY 24 24,‖ 2019.

J. W. Lin, C. T. Chao, and J. S. Chiou, ―Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg-Marquardt Algorithm to minimise backpropagation errors,‖ Geosci. Instrumentation, Methods Data Syst., vol. 7, no. 3, pp. 235–243, 2018, doi: 10.5194/gi-7-235-2018.

M. Böse, F. Wenzel, and M. Erdik, ―PreSEIS: A neural network-based approach to earthquake early warning for finite faults,‖ Bull. Seismol. Soc. Am., vol. 98, no. 1, pp. 366–382, 2008, doi: 10.1785/0120070002.

S. Gentili and A. Michelini, ―Automatic picking of P and S phases using a neural tree,‖ J. Seismol., vol. 10, no. 1, pp. 39–63, 2006, doi: 10.1007/s10950-006-2296-6.

M. Moustra, M. Avraamides, and C. Christodoulou, ―Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals,‖ Expert Syst. Appl., vol. 38, no. 12, pp. 15032–15039, Nov. 2011, doi: 10.1016/j.eswa.2011.05.043.

N. R. Sari, W. F. Mahmudy, and A. P. Wibawa, ―Backpropagation on neural network method for inflation rate forecasting in Indonesia,‖ Int. J. Adv. Soft Comput. its Appl., vol. 8, no. 3, 2016.

F. A. Huda, W. F. Mahmudy, and H. Tolle, ―Android malware detection using backpropagation neural network,‖ Indones. J. Electr. Eng. Comput. Sci., vol. 4, no. 1, 2016, doi: 10.11591/ijeecs.v4.i1.pp240-244.

H. Aini and H. Haviluddin, ―Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach,‖ Knowl. Eng. Data Sci., vol. 2, no. 1, pp. 1–9, 2019.

M. Romano et al., ―Artificial neural network for tsunami forecasting,‖ J. Asian Earth Sci., vol. 36, no. 1, pp. 29–37, 2009, doi: 10.1016/j.jseaes.2008.11.003.

C. J. Lin, Z. Shen, and S. Huang, ―Predicting Structural Response with On-Site Earthquake Early Warning System Using Neural Networks,‖ Weather, no. 226, 2011.

J. Schmidhuber, ―Deep Learning in neural networks: An overview,‖ Neural Networks, vol. 61, pp. 85–117, 2015, doi: 10.1016/j.neunet.2014.09.003.

G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, ―Continual lifelong learning with neural networks: A review,‖ Neural Networks, vol. 113, pp. 54–71, May 2019, doi: 10.1016/j.neunet.2019.01.012.

G. T. Hicham, E. A. Chaker, and E. Lotfi, ―Comparative study of neural networks algorithms for cloud computing CPU scheduling,‖ Int. J. Electr. Comput. Eng., vol. 7, no. 6, pp. 3570–3577, 2017, doi: 10.11591/ijece.v7i6.pp3570-3577.

C. Dewi, S. Sundari, and M. Mardji, ―Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network,‖ J. Inf. Technol. Comput. Sci., vol. 4, no. 1, pp. 1–14, 2019.

K. Chandrasekaran and S. P. Simon, ―Binary/real coded particle swarm optimization for unit commitment problem,‖ in International Conference on Power, Signals, Controls and Computation, Jan. 2012, no. 3, pp. 1–6, doi: 10.1109/EPSCICON.2012.6175240.

A. T. C. Goh, ―Back-propagation neural networks for modeling complex systems,‖ Artif. Intell. Eng., vol. 9, no. 3, pp. 143–151, Jan. 1995, doi: 10.1016/0954-1810(94)00011-S.

M. Riedmiller and H. Braun, ―A direct adaptive method for faster backpropagation learning: the RPROP algorithm,‖ in IEEE International Conference on Neural Networks, 1993, pp. 586–591, doi: 10.1109/ICNN.1993.298623.

K. Mogi, ―Earthquake Prediction in Japan,‖ J. Phys. Earth, vol. 43, no. 5, pp. 533–561, 1995.

U.S. Geological Survey, ―What is an earthquake and what causes them to happen?,‖ U.S. Department of the Interior, 2019.

Incorporated Research Institutions for Seismology (IRIS), ―Seismic Wave Behavior — Effect on Buildings‖ .

Incorporated Research Institutions for Seismology (IRIS), ―3-Component Seismograph,‖ 2017.

A. S. N. Alarifi, N. S. N. Alarifi, and S. Al-Humidan, ―Earthquakes magnitude predication using artificial neural

network in northern Red Sea area,‖ J. King Saud Univ. - Sci., vol. 24, no. 4, pp. 301–313, Oct. 2012, doi: 10.1016/j.jksus.2011.05.002.

I. Wahyuni, N. R. Adam, W. F. Mahmudy, and A. Iriany, ―Modeling backpropagation neural network for rainfall prediction in tengger east Java,‖ in Proceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017, 2018, vol. 2018-Janua, doi: 10.1109/SIET.2017.8304130.



  • There are currently no refbacks.

Copyright (c) 2020 Knowledge Engineering and Data Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Flag Counter

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats