Application of Artificial Neural Network Methods to Anatolian Plate Earthquake Magnitude and Location Prediction

Year 2024, Volume: 9 Issue: 2, 47 - 62

Murat Emeç Mehmet Hilal Özcanhan

https://doi.org/10.30931/jetas.1377481

Abstract

Same-region earthquakes usually have a pattern that is difficult to identify clearly. Therefore, time series analysis methods have been proposed for earthquake prediction. Our work attempts to predict three earthquake parameters in the Anatolian Peninsula using pure artificial neural network methods. An optimized BP-NN model and optimally hyper-parameterized LSTM Model have been designed to predict earthquake magnitude, latitude, and longitude. The two models are compared with previous works for their prediction performances using four well-accepted metrics: mean squared error, mean absolute error, median absolute error, and standard deviation. The time, depth, sun, and moon distances to Earth were identified as the most contributing factors in earthquake occurrence through analysis by five different feature extraction algorithms. The date harmed the prediction accuracy. The LSTM model outperformed the BP-NN Model in magnitude prediction with 0.062 MSE. Latitude predictions of both methods were satisfactory and close. However, BP-NN had lower error rates in latitude prediction. However, longitude prediction errors were significant in both models. Therefore, our designs did not successfully predict the exact location of the earthquakes. However, multi-variate, stacked LSTM models are promising in predicting Anatolian Peninsula earthquake magnitudes, but future work is necessary for location and timing predictions.

Keywords

Artificial Neural Networks, Earthquakes, Prediction methods, Back Propagation, LSTM

References

• [1] Sobolev, G.A., "Methodology, results, and problem forecasting earthquakes", Her. Russ. Acad. Sci. 85 (2015) : 107–111
• [2] Wang, Q., Guo, Y., Yu, L., Li, P., "Earthquake prediction based on spatiotemporal data mining: an LSTM network approach", IEEE Transactions on Emerging Topics in Computing 8 (1) (2017) : 148-158.
• [3] Narayanakumar, S., Raja, K., "A BP artificial neural network model for earthquake magnitude prediction in Himalayas, India", Circuits and Systems 7 (11) (2016) : 3456-3468.
• [4] Last, M., Rabinowitz, N., Leonard, G., "Predicting the maximum earthquake magnitude from seismic data in Israel and its neighboring countries", PloS one 11 (1) (2016) : e0146101.
• [5] Mahmoudi, J., Arjomand, M. A., Rezaei, M., Mohammadi, M. H., "Predicting the earthquake magnitude using the multilayer perceptron neural network with two hidden layers", Civil engineering journal 2 (1) (2016) : 1-12.
• [6] Li, C., & Liu, X., "An improved PSO-BP neural network and its application to earthquake prediction", Chinese Control and Decision Conference (CCDC) IEEE (2016) : 3434-3438
• [7] Saba, S., Ahsan, F., Mohsin, S., "BAT-ANN based earthquake prediction for Pakistan region", Soft Computing 21 (2017) : 5805-5813.
• [8] Asencio-Cortés, G., Martínez-Álvarez, F., Morales-Esteban, A., Reyes, J., & Troncoso, A., Improving earthquake prediction with principal component analysis: application to Chile", In: Hybrid Artificial Intelligent Systems: 10th International Conference, HAIS 2015, Bilbao, Spain, Springer International Publishing 10 (2015) : 393-404.
• [9] Scholz, C. H., "A physical interpretation of the Haicheng earthquake prediction", Nature 267(5607) (1977) : 121-124.
• [10] Dahmen, K., Ertaş, D., Ben-Zion, Y., "Gutenberg-Richter and characteristic earthquake behavior in simple mean-field models of heterogeneous faults", Physical Review E 58(2), (1998) : 1494.
• [11] Boucouvalas, A. C., Gkasios, M., Tselikas, N. T., & Drakatos, G., "Modified-Fibonacci-Dual-Lucas method for earthquake prediction", In: Third international conference on remote sensing and geoinformation of the environment (RSCy2015) 9535 (2015) : 400-410. SPIE.
• [12] Akhoondzadeh, M., Chehrebargh, F. J., "Feasibility of anomaly occurrence in aerosols time series obtained from MODIS satellite images during hazardous earthquakes", Advances in Space Research, 58(6) (2016) : 890-896.
• [13] Hayakawa, M., "Earthquake prediction with electromagnetic phenomena", In: AIP Conference Proceedings, AIP Publishing 1709(1) (2016)
• [14] Hayakawa, M., Yamauchi, H., Ohtani, N., Ohta, M., Tosa, S., Asano, T., ... Eftaxias, K., "On the precursory abnormal animal behavior and electromagnetic effects for the Kobe earthquake (M~ 6) on April 12, 2013", Open Journal of Earthquake Research, 5(03) (2016) : 165.
• [15] Fan, J., Chen, Z., Yan, L., Gong, J., Wang, D., "Research on earthquake prediction from infrared cloud images", In: MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications SPIE 9815 (2015) : 87-92.
• [16] Thomas, J. N., Masci, F., Love, J. J., "On a report that the 2012 M 6.0 earthquake in Italy was predicted after seeing an unusual cloud formation", Natural Hazards and Earth System Sciences 15 (5) (2015) : 1061-1068.
• [17] Florido, E., Martínez-Álvarez, F., Morales-Esteban, A., Reyes, J., Aznarte-Mellado, J. L., "Detecting precursory patterns to enhance earthquake prediction in Chile", Computers & geosciences 76 (2015) : 112-120.
• [18] Asencio-Cortés, G., Martínez-Álvarez, F., Morales-Esteban, A., Reyes, J., "A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction", Knowledge-Based Systems 101 (2016) : 15-30.
• [19] Morales-Esteban, A., Martínez-Álvarez, F., Troncoso, A., Justo, J. L., Rubio-Escudero, C., "Pattern recognition to forecast seismic time series", Expert systems with applications 37 (12), (2010) : 8333-8342.
• [20] Panakkat, A., Adeli, H., "Neural network models for earthquake magnitude prediction using multiple seismicity indicators", International journal of neural systems 17 (1), (2007) : 13-33.
• [21] Ikram, A., Qamar, U., "A rule-based expert system for earthquake prediction", Journal of Intelligent Information Systems 43 (2014) : 205-230.
• [22] Moustra, M., Avraamides, M., Christodoulou, C., "Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals", Expert systems with applications 38 (12), (2011) : 15032-15039.
• [23] Li, C., Liu, X., "An improved PSO-BP neural network and its application to earthquake prediction", In: Chinese Control and Decision Conference (CCDC) IEEE (2016) : 3434-3438.
• [24] Narayanakumar, S., Raja, K., "A BP artificial neural network model for earthquake magnitude prediction in Himalayas, India", Circuits and Systems 7 (11) (2016) : 3456-3468.
• [25] Kim, J., & Moon, N., "BiLSTM model based on multivariate time series data in multiple fields for forecasting trading area", Journal of Ambient Intelligence and Humanized Computing, (2019) : 1-10.
• [26] Mignan, A., Broccardo, M., "Neural network applications in earthquake prediction (1994–2019): Meta‐analytic and statistical insights on their limitations", Seismological Research Letters 91 (4) (2020) : 2330-2342.
• [27] Mousavi, S. M., Zhu, W., Sheng, Y., Beroza, G. C., "CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection", Scientific reports 9 (1) (2019) : 10267.
• [28] Kail, R., Burnaev, E., Zaytsev, A., "Recurrent convolutional neural networks help to predict location of earthquakes", IEEE Geoscience and Remote Sensing Letters 19 (2021) : 1-5.
• [29] McHugh, C. M., Seeber, L., Cormier, M. H., Dutton, J., Cagatay, N., Polonia, A., ... Gorur, N., "Submarine earthquake geology along the North Anatolia Fault in the Marmara Sea, Turkey: a model for transform basin sedimentation", Earth and Planetary Science Letters 248 (3-4) (2006) : 661-684.
• [30] Tan, O., Tapirdamaz, M. C., Yörük, A., "The earthquake catalogues for Turkey", Turkish Journal of Earth Sciences 17 (2) (2008) : 405-418.
• [31] Reilinger, R. E., McClusky, S. C., Oral, M. B., King, R. W., Toksoz, M. N., Barka, A. A., ... & Sanli, I., "Global Positioning System measurements of present‐day crustal movements in the Arabia‐Africa‐Eurasia plate collision zone", Journal of Geophysical Research: Solid Earth 102 (B5) (1997) : 9983-9999.
• [32] Sparavigna, A. C., "Software applied to archaeoastronomy: SunCalc and MoonCalc at the Torhouse Stone Circle", PHILICA (2017) : (1134).
• [33] Yang, J., Liu, L., Jiang, T., Fan, Y., "A modified Gabor filter design method for fingerprint image enhancement", Pattern Recognition Letters 24 (12) (2003) : 1805-1817.
• [34] Sztandera, L. M., "Tactile fabric comfort prediction using regression analysis", Wseas Transactions on Computers 2 (8) (2009) : 292-301.
• [35] Masters, T., "Practical neural network recipes in C++", Morgan Kaufmann (1993).
• [36] Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J., "A novel connectionist system for unconstrained handwriting recognition", IEEE transactions on pattern analysis and machine intelligence 31 (5) (2008) : 855-868.
• [37] Graves, A., Mohamed, A. R., Hinton, G., "Speech recognition with deep recurrent neural networks", In: IEEE international conference on acoustics, speech and signal processing IEEE (2013) : 6645-6649.
• [38] Chauhan, S., Vig, L., "Anomaly detection in ECG time signals via deep long short-term memory networks", In: IEEE international conference on data science and advanced analytics (DSAA) IEEE (2015) : 1-7.
• [39] Shewalkar, A., Nyavanandi, D., Ludwig, S. A., "Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU", Journal of Artificial Intelligence and Soft Computing Research 9 (4) (2019) : 235-245.
• [40] Yu, Y., Si, X., Hu, C., Zhang, J., "A review of recurrent neural networks: LSTM cells and network architectures", Neural computation 31 (7) 2019) : 1235-1270.
• [41] Chung, J., Gulcehre, C., Cho, K., Bengio, Y., "Empirical evaluation of gated recurrent neural networks on sequence modeling", arXiv preprint 1412.3555 (2014).