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An Earthquake Prediction System for Bangladesh Using Deep Long Short-Term Memory Architecture

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Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 185))

Abstract

Earthquake is a natural catastrophe, which is one of the most significant causes of structural and financial damage, along with the death of many humans. Prediction of the earthquake at least a month ahead of the event may diminish the death toll and financial loss. Bangladesh is in an active seismic region, where many earthquakes with small and medium magnitude occur almost every year. Several scientists have predicted that there is a good chance of an earthquake with startling energy shortly in this region. In this work, we have proposed a long short-term memory (LSTM)-based architecture for earthquake prediction in Bangladesh in the following month. After tuning hyperparameters, an architecture of 2 LSTM layers with 200 and 100 neurons, respectively, along with L1 and L2 regularization, was found to be the most efficient. The activation functions of the LSTM layers were tanh in the proposed architecture. The proposed LSTM architecture achieved a remarkable 70.67% accuracy with 64.78% sensitivity, 75.94% specificity in earthquake prediction for this region.

Md. Hasan Al Banna and Tapotosh Ghosh are contributed equally.

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Acknowledgements

This study was supported by the research funds of the Information and Communication Technology division of the Government of the People’s Republic of Bangladesh in 2019 - 2020.

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Authors and Affiliations

  1. Bangladesh University of Professionals, Dhaka, Bangladesh

    Md. Hasan Al Banna, Tapotosh Ghosh & Kazi Abu Taher

  2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M. Shamim Kaiser

  3. Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK

    Mufti Mahmud

Authors
  1. Md. Hasan Al Banna
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  2. Tapotosh Ghosh
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  3. Kazi Abu Taher
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  4. M. Shamim Kaiser
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  5. Mufti Mahmud
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Corresponding author

Correspondence to M. Shamim Kaiser .

Editor information

Editors and Affiliations

  1. School of computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Siba K. Udgata

  2. IGIT Sarang, Dhenkanal, Odisha, India

    Srinivas Sethi

  3. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Satish N. Srirama

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Hasan Al Banna, M., Ghosh, T., Taher, K.A., Kaiser, M.S., Mahmud, M. (2021). An Earthquake Prediction System for Bangladesh Using Deep Long Short-Term Memory Architecture. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_41

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