Abstract
Earthquakes are one of nature's most devastating disasters. Earthquake prediction is critical in seismology since its success can save lives, property, and infrastructure. Numerous technologies have been proposed to address this issue, including mathematical analysis, artificial intelligence, and machine learning algorithms. Unfortunately, due to earthquakes' dynamic and spontaneous nature, they frequently fail to provide positive results. The study uses deep learning techniques to predict the magnitude of an impending earthquake using eight mathematically calculated seismic indicators derived from Japan, Indonesia, and the Hindu-Kush Karakoram Himalayan (HKKH) region's earthquake catalogs. Three deep learning techniques, including Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM), and self-attention-based transformer, have been implemented to model the associations between calculated seismic indicators and potential earthquake incidents. These models have been evaluated with well-known matrices such as the Mean Absolute Error (MAE), Mean Squared Error (MSE), log-cosh loss, and Mean Squared Logarithmic Error (MSLE). The value of these cost functions converges to a small number for all models, indicating that these models effectively predict earthquake magnitudes. When these models were fed with an unknown test dataset from Japan, the LSTM model performed best with the least deviation metrics (MAE = 0.060, MSE = 0.006, log cosh = 0.042 and MSLE = 0.003). Similarly, the Bi-LSTM model delivered the ideal result for the Indonesia earthquake catalog (MAE = 0.073, MSE = 0.009, log cosh = 0.016, and MSLE = 0.009), while the transformer model produced the optimal result for the HKKH region (MAE = 0.062, MSE = 0.006, log cosh = 0.043, and MSLE = 0.003). Predicting earthquake magnitude at various locations using these methodologies produces significant and positive results for magnitudes ranging from 3.5 M to 6.0 M, paving the way for the ultimate robust prediction mechanism, that has not yet been developed.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Code availability
All codes for data cleaning and analysis associated with the current submission are available from the corresponding author on reasonable request.
References
Adeli H, Panakkat A (2009) A probabilistic neural network for earthquake magnitude prediction. Neural Netw 22(7):1018–1024. https://doi.org/10.1016/j.neunet.2009.05.003
Ahumada A, Altunkaynak A, Ayoub A (2015) Fuzzy logic-based attenuation relationships of strong motion earthquake records. Expert Syst Appl 42(3):1287–1297. https://doi.org/10.1016/j.eswa.2014.09.035
Asencio-Cortés G, Morales-Esteban A, Shang X, Martínez-Álvarez F (2018) Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput Geosci 115:198–210. https://doi.org/10.1016/j.cageo.2017.10.011
Asim KM, Martínez-Álvarez F, Basit A, Iqbal T (2017) Earthquake magnitude prediction in Hindukush region using machine learning techniques. Nat Hazards 85(1):471–486. https://doi.org/10.1007/s11069-016-2579-3
Banna MdHA et al (2021) Attention-based Bi-Directional long-short term memory network for earthquake prediction. IEEE Access 9:56589–56603. https://doi.org/10.1109/ACCESS.2021.3071400
Bartels SA, VanRooyen MJ (2012) Medical complications associated with earthquakes. Lancet 379(9817):748–757. https://doi.org/10.1016/S0140-6736(11)60887-8
Bhandarkar T, K V, Satish N, Sridhar S, Sivakumar R, Ghosh S (2019) Earthquake trend prediction using long short-term memory RNN. IJECE 9(2):1304. https://doi.org/10.11591/ijece.v9i2.pp1304-1312
Berhich A, Belouadha F-Z, Kabbaj MI (2020) LSTM-based models for earthquake prediction. In: Proceedings of the 3rd international conference on networking, information systems & security, New York, NY, USA, pp 1–7. https://doi.org/10.1145/3386723.3387865
Cao K, Huang Q (2018) Geo-sensor(s) for potential prediction of earthquakes: can earthquake be predicted by abnormal animal phenomena? Ann GIS 24(2):125–138. https://doi.org/10.1080/19475683.2018.1450785
Cui Z, Ke R, Pu Z, Wang Y (2020) Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transp Res Part C: Emerg Technol 118:102674. https://doi.org/10.1016/j.trc.2020.102674
DeVries PMR, Viégas F, Wattenberg M, Meade BJ (2018) Deep learning of aftershock patterns following large earthquakes. Nature 560(7720):632–634. https://doi.org/10.1038/s41586-018-0438-y
Florido E, Martínez-Álvarez F, Morales-Esteban A, Reyes J, Aznarte-Mellado JL (2015) Detecting precursory patterns to enhance earthquake prediction in Chile. Comput Geosci 76:112–120. https://doi.org/10.1016/j.cageo.2014.12.002
González J, Yu W, Telesca L (2019) Earthquake magnitude prediction using recurrent neural networks. Proceedings 24(1):Art. no. 1. https://doi.org/10.3390/IECG2019-06213
Goswami S, Chakraborty S, Ghosh S, Chakrabarti A, Chakraborty B (2018) A review on application of data mining techniques to combat natural disasters. Ain Shams Eng J 9(3):365–378. https://doi.org/10.1016/j.asej.2016.01.012
Grover P (2021) 5 regression loss functions all machine learners should know. Medium. https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0. Accessed 31 Aug 2021
Gutenberg B, Richter CF (1956) Earthquake magnitude, intensity, energy, and acceleration. Bull Seismol Soc Am 46(2):105–145. https://doi.org/10.1785/BSSA0460020105
Hartmann J, Levy JK (2005) Hydrogeological and gasgeochemical earthquake precursors ? A review for application. Nat Hazards 34(3):279–304. https://doi.org/10.1007/s11069-004-2072-2
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang J, Wang X, Zhao Y, Xin C, Xiang H (2018) Large earthquake magnitude prediction in Taiwan based on deep learning neural network. NNW 28(2):149–160. https://doi.org/10.14311/NNW.2018.28.009
Huang Y, Han X, Zhao L (2021) Recurrent neural networks for complicated seismic dynamic response prediction of a slope system. Eng Geol 289:106198. https://doi.org/10.1016/j.enggeo.2021.106198
Ikram A, Qamar U (2014) A rule-based expert system for earthquake prediction. J Intell Inf Syst 43(2):205–230. https://doi.org/10.1007/s10844-014-0316-5
Jais IKM, Ismail AR, Nisa SQ (2019) Adam optimization algorithm for wide and deep neural network. Knowl Eng Data Sci 2(1):Art. no. 1. https://doi.org/10.17977/um018v2i12019p41-46
Jena R, Pradhan B, Naik SP, Alamri AM (2021) Earthquake risk assessment in NE India using deep learning and geospatial analysis. Geosci Front 12(3):101110. https://doi.org/10.1016/j.gsf.2020.11.007
Kingma DP, Ba LJ (2015) Adam: a method for stochastic optimization. Accessed: 01 Oct 2021. [Online]. Available: https://dare.uva.nl/search?identifier=a20791d3-1aff-464a-8544-268383c33a75
Li R, Lu X, Li S, Yang H, Qiu J, Zhang L (2020) DLEP: a deep learning model for earthquake prediction. In: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, pp 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207621
Maji C, Sadhukhan B, Mukherjee S, Khutia S, Chaudhuri H (2021) Impact of climate change on seismicity:a statistical approach. Arab J Geosci 14(24):2725. https://doi.org/10.1007/s12517-021-08946-8
Mallouhy R, Abou Jaoude C, Guyeux C, Makhoul A (2019) Major earthquake event prediction using various machine learning algorithms. In: International Conference on Information and Communication Technologies for Disaster Management, Paris, France. Accessed: 18 Nov 2022. [Online]. Available: https://hal.archives-ouvertes.fr/hal-02952670
Mean Squared Logarithmic Error Loss. InsideAIML. https://insideaiml.com/blog/MeanSquared-Logarithmic-Error-Loss-1035. Accessed 08 Nov 2021
Mignan A, Broccardo M (2019) One neuron versus deep learning in aftershock prediction. Nature 574(7776):Art. no. 7776. https://doi.org/10.1038/s41586-019-1582-8
Otari G, Kulkarni R (2012) A review of application of data mining in earthquake prediction. Undefined. Accessed: 17 Aug 2021. [Online]. Available: https://www.semanticscholar.org/paper/A-Review-of-Application-of-Data-Mining-in-Otari-Kulkarni/9d97b76f386bae092aeaa98c062552d14e2e0f84
Panakkat A, Adeli H (2007) Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int J Neur Syst 17(01):13–33. https://doi.org/10.1142/S0129065707000890
Panakkat A, Adeli H (2009) Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput-Aided Civ Infrastruct Eng 24(4):280–292. https://doi.org/10.1111/j.1467-8667.2009.00595.x
Parikh AP, Täckström O, Das D, Uszkoreit J (2016) A decomposable attention model for natural language inference. arXiv:1606.01933[cs], Accessed: 20 Aug 2021. [Online]. Available: http://arxiv.org/abs/1606.01933
Petraki E et al (2015) Radon-222: a potential short-term earthquake precursor. J Earth Sci Clim Chang 06(06). https://doi.org/10.4172/2157-7617.1000282
Pishro-Nik H (2014) Introduction to probability, statistics and random processes. [Online]. Available: https://scholarworks.umass.edu/ece_ed_materials/1
Pulinets S (2004) Ionospheric precursors of earthquakes; recent advances in theory and practical applications. Terr Atmos Ocean Sci 15(3):413. https://doi.org/10.3319/TAO.2004.15.3.413(EP)
Qiu J, Wang B, Zhou C (2020) Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE 15(1):e0227222. https://doi.org/10.1371/journal.pone.0227222
Saba S, Ahsan F, Mohsin S (2017) BAT-ANN based earthquake prediction for Pakistan region. Soft Comput 21(19):5805–5813. https://doi.org/10.1007/s00500-016-2158-2
Sadhukhan B, Chakraborty S, Mukherjee S (2021a) Investigating the relationship between earthquake occurrences and climate change using RNN-based deep learning approach. Arab J Geosci 15(1):31. https://doi.org/10.1007/s12517-021-09229-y
Sadhukhan B, Mukherjee S, Banerjee S, Samanta RK (2021b) Multifractal, nonlinear, and chaotic nature of earthquake and global temperature. Arab J Geosci 14(17):1811. https://doi.org/10.1007/s12517-021-08153-5
Sadhukhan B, Mukherjee S, Sarkar D, Samanta RK (2021c) Investigating the relationship between earthquake occurrences and global temperature fluctuation patterns. Arab J Geosci 14(18):1932. https://doi.org/10.1007/s12517-021-08296-5
Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681. https://doi.org/10.1109/78.650093
Tucker BE (2013) Reducing earthquake risk. Science 341(6150):1070–1072. https://doi.org/10.1126/science.1239236
Vaswani A et al (2017) Attention is all you need. In: Advances in neural information processing systems, vol. 30. Accessed: 20 Aug 2021. [Online]. Available: https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Wang Q, Guo Y, Yu L, Li P (2020) Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. IEEE Trans Emerg Topics Comput 8(1):148–158. https://doi.org/10.1109/TETC.2017.2699169
Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res 30(1):79–82
Wu J, Chen X-Y, Zhang H, Xiong L-D, Lei H, Deng S-H (2019) Hyperparameter optimization for machine learning models based on bayesian optimization. J Electron Sci Technol 17(1):26–40. https://doi.org/10.11989/JEST.1674-862X.80904120
Yousefzadeh M, Hosseini SA, Farnaghi M (2021) Spatiotemporally explicit earthquake prediction using deep neural network. Soil Dyn Earthq Eng 144:106663. https://doi.org/10.1016/j.soildyn.2021.106663
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Bikash Sadhukhan and Shayak Chakraborty. The first draft of the manuscript was written by Bikash Sadhukhan. All authors read and approved the final manuscript.
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Sadhukhan, B., Chakraborty, S. & Mukherjee, S. Predicting the magnitude of an impending earthquake using deep learning techniques. Earth Sci Inform 16, 803–823 (2023). https://doi.org/10.1007/s12145-022-00916-2
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DOI: https://doi.org/10.1007/s12145-022-00916-2