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An Attentional Model for Earthquake Prediction Using Seismic Data

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Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection (PAAMS 2021)

Abstract

Natural disaster prediction is one of the main concerns of authorities globally. Disasters cause large-scale psychological, social, and economic damage; therefore, techniques to predict such events are essential to minimize their impacts. However, despite all efforts to estimate the occurrence of a disaster, making an accurate and robust forecast is a challenging task. In recent years, Deep Learning techniques have innovated several fields by learning the factors that contribute to the phenomena generation; also, biologically inspired concepts such as attention have provided cheap ways to allocate computational resources by consuming only the necessary information to solve the task. This work aims to develop and evaluate a feature-based temporal attentional model to predict the time remaining for an earthquake event by consuming seismic waves from the LANL Earthquake dataset. The proposed models comprehend four distinct architectures based on 1D-CNN, LSTM, and LSTM with self-attention mechanisms. Experimental results indicated that attention plays a vital role in learning temporal relations, with models achieving state-of-the-art results in the task.

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Notes

  1. 1.

    https://www.kaggle.com/c/LANL-Earthquake-Prediction/data.

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Acknowledgment

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

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de Santana Correia, A., Cleveston, I., dos Santos, V.B., Avila, S., Colombini, E.L. (2021). An Attentional Model for Earthquake Prediction Using Seismic Data. In: De La Prieta, F., El Bolock, A., Durães, D., Carneiro, J., Lopes, F., Julian, V. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection. PAAMS 2021. Communications in Computer and Information Science, vol 1472. Springer, Cham. https://doi.org/10.1007/978-3-030-85710-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-85710-3_5

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  • Print ISBN: 978-3-030-85709-7

  • Online ISBN: 978-3-030-85710-3

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