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Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models

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Abstract

The main purpose of this study is to compare the performance of two statistical analysis models like weight of evidence and logistic regression (LR) with a soft computing model like artificial neural networks for landslide susceptibility assessment. These models were applied for the Selinous River drainage basin (northern Peloponnese, Greece) in order to map landslide susceptibility and rate the importance of landslide causal factors. A landslide inventory was prepared using satellite imagery interpretation and field surveys. Eight causal factors including altitude, slope angle, slope aspect, distance to road network, distance to drainage network, distance to tectonic elements, land cover, and lithology were considered. Model performance was tested with receiver operator characteristic analysis. The validation findings revealed that the three models show promising results since they give good accuracy values. However, the LR model proved to be relatively superior in estimating landslide susceptibility throughout the study area.

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Acknowledgements

The research leading to these results receives funding from the Hellenic Foundation for Research and Innovation (HFRI) of the General Secretariat for Research and Technology (GSRT). The authors also express their gratitude to the anonymous reviewers for their valuable comments which markedly improved the manuscript.

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  1. Department of Geography, Harokopio University, El. Venizelou 70, 17671, Athens, Greece

    Christos Polykretis & Christos Chalkias

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Polykretis, C., Chalkias, C. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Nat Hazards 93, 249–274 (2018). https://doi.org/10.1007/s11069-018-3299-7

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