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Hybrid Computational Intelligence Modeling of Coseismic Landslides’ Severity

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

Coseismic Landslides (COLA) are one of the most widespread and destructive hazards to result from earthquakes in mountainous environments. They are always associated with almost instantaneous slope collapse and spreading, posing significant hazards to human lives and lifeline facilities worldwide. Current methods to identify COLA immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Their realistic prediction is crucial for the design of key infrastructure and to protect human lives in seismically active regions. Forecasting their severity could be extremely beneficial for the effective treatment of disastrous consequences. The goal of this research is to propose a hybrid model that takes into consideration only three of the most affordable factors to acquire, the Slope of the active areas, the Aspect and the Geological Form. Determination of their correlation could predict the severity of COLA phenomena. The dataset used in this research, comprises of 421 records for year 2003 and 767 for 2015 from the Greek island of Lefkada. The introduced hybrid model employs Fuzzy c-Means, Ensemble Adaptive Boosting and Ensemble Subspace k-Nearest Neighbor algorithms. The model managed to successfully classify the Coseismic Landslides according to their severity. The performance was high especially for the classes of major severity.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, Democritus University of Thrace, 67100, Xanthi, Greece

    Anastasios Panagiotis Psathas, Antonios Papaleonidas, George Papathanassiou & Lazaros Iliadis

  2. Koronidos str 9, 42131, Trikala, Greece

    Sotirios Valkaniotis

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  1. Anastasios Panagiotis Psathas
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  2. Antonios Papaleonidas
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  3. George Papathanassiou
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  4. Lazaros Iliadis
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  5. Sotirios Valkaniotis
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Correspondence to Anastasios Panagiotis Psathas .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Psathas, A.P., Papaleonidas, A., Papathanassiou, G., Iliadis, L., Valkaniotis, S. (2021). Hybrid Computational Intelligence Modeling of Coseismic Landslides’ Severity. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88080-4

  • Online ISBN: 978-3-030-88081-1

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