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Landslide Susceptibility Assessment Using Binary Logistic Regression in Northern Philippines

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Understanding and Reducing Landslide Disaster Risk (WLF 2020)

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Abstract

In a landslide prone area in mountainous northern Philippines, landslide susceptibility using binary logistic regression was investigated. Landslide data were randomly divided into training and validation sets using 80% and 20% proportions, respectively. A detailed logistic regression procedure was applied and presented herein. Nine landslide conditioning factors were used. Based on the coefficients obtained, the most influential factors were NDVI followed by land use/land cover, slope aspect, lithology and slope angle. Distance to lineament, distance to road, plan and profile curvature showed no influence in the model generated. Training and validation accuracy were good, amounting to 91% and 86%, respectively. Using training data, 80%, 11%, 5%, 2% and 2% of the landslides were associated with the very high, high, moderate, low and very low susceptibility classes, respectively. Using validation data, the proportions were 82%, 10%, 5%, 3% and 0%, respectively. The strong influence of NDVI affirms its major role in modelling landslide susceptibility. It supports the strong potential of revegetation of precarious slopes in complementing ongoing structural slope stabilization and rehabilitation measures.

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Acknowledgements

The authors would like to thank the University of New England and the University of the Philippines Baguio for supporting this research which was part of the dissertation of DNJ at UNE. We are grateful also to the government officials and residents of the municipality of Tublay for the fieldwork support extended.

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Authors and Affiliations

  1. College of Science, University of the Philippines Baguio, Governor Pack Road, 2600, Baguio City, Philippines

    Dymphna Nolasco-Javier

  2. School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia

    Lalit Kumar

Authors
  1. Dymphna Nolasco-Javier
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  2. Lalit Kumar
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Corresponding author

Correspondence to Dymphna Nolasco-Javier .

Editor information

Editors and Affiliations

  1. Department of Civil Protection, Rome, Italy

    Fausto Guzzetti

  2. Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Zagreb, Croatia

    Snježana Mihalić Arbanas

  3. Research institute for Geo-Hydrological Protection (Italian National Research Council), Perugia, Italy

    Paola Reichenbach

  4. International Consortium on Landslides, Kyoto, Japan

    Kyoji Sassa

  5. Geological Survey of Canada, Sidney, BC, Canada

    Peter T. Bobrowsky

  6. Graduate School of Advanced Integrated Studies in Human Survivability (Shishu-kan), Kyoto University, Kyoto, Japan

    Kaoru Takara

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Nolasco-Javier, D., Kumar, L. (2021). Landslide Susceptibility Assessment Using Binary Logistic Regression in Northern Philippines. In: Guzzetti, F., Mihalić Arbanas, S., Reichenbach, P., Sassa, K., Bobrowsky, P.T., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60227-7_20

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