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A Comparative Study of Over-Sampling Techniques as Applied to Seismic Events

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Artificial Intelligence Research (SACAIR 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1976))

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Abstract

The likelihood that an earthquake will occur in a specific location, within a specific time frame, and with ground motion intensity greater than a specific threshold is known as a seismic hazard. Predicting these types of hazards is crucial since doing so can enable early warnings, which can lessen the negative effects. Research is currently being executed in the field of machine learning to predict seismic events based on previously recorded incidents. However, because these events happen so infrequently, this presents a class imbalance problem to the machine learning or deep learning learners. As a result, this study provided a comparison of the performance of popular over-sampling techniques that seek to even out class imbalance in seismic events data. Specifically, this work applied SMOTE, SMOTENC, SMOTEN, BorderlineSMOTE, SVMSMOTE, and ADASYN to an open source Seismic Bumps dataset then trained several machine learning classifiers with stratified K-fold cross-validation for seismic hazard detection. The SVMSMOTE algorithm was the best over-sampling method as it produced classifiers with the highest overall accuracy, F1 score, recall, and precision of 100%, respectively, whereas the ADASYN over-sampling methodology showed the lowest performance in all the reported metrices of all the models. To our understanding, no research has been done comparing the effectiveness of the aforementioned over-sampling techniques for tasks involving seismic events.

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Authors and Affiliations

  1. Council for Scientific and Industrial Research Cluster: Next Generation Enterprises and Institutions, Data Science, Pretoria, South Africa

    Mpho Mokoatle, Toshka Coleman & Paul Mokilane

  2. University of Pretoria, Pretoria, South Africa

    Mpho Mokoatle

Authors
  1. Mpho Mokoatle
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  2. Toshka Coleman
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  3. Paul Mokilane
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Correspondence to Mpho Mokoatle .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Anban Pillay

  2. University of KwaZulu-Natal, Durban, South Africa

    Edgar Jembere

  3. University of the Western Cape, Cape Town, South Africa

    Aurona J. Gerber

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Mokoatle, M., Coleman, T., Mokilane, P. (2023). A Comparative Study of Over-Sampling Techniques as Applied to Seismic Events. In: Pillay, A., Jembere, E., J. Gerber, A. (eds) Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science, vol 1976. Springer, Cham. https://doi.org/10.1007/978-3-031-49002-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-49002-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49001-9

  • Online ISBN: 978-3-031-49002-6

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