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Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability

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Abstract

Statistical landslide susceptibility mapping is a topic in complete and constant evolution, especially since the introduction of machine learning (ML) methods. A new methodological approach is here presented, based on the ensemble of artificial neural network, generalized boosting model and maximum entropy ML algorithms. Such approach has been tested in the Monterosso al Mare area, Cinque Terre National Park (Northern Italy), severely hit by landslides in October 2011, following an extraordinary precipitation event, which caused extensive damage at this World Heritage site. Thirteen predisposing factors were selected and assessed according to the main characteristics of the territory and through variance inflation factor, whilst a database made of 260 landslides was adopted. Four different Ensemble techniques were applied, after the averaging of 300 stand-alone methods, each one providing validation scores such as ROC (receiver operating characteristics)/AUC (area under curve) and true skill statistics (TSS). A further model performance evaluation was achieved by assessing the uncertainty through the computation of the coefficient of variation (CV). Ensemble modelling thus showed improved reliability, testified by the higher scores, by the low values of CV and finally by a general consistency between the four Ensemble models adopted. Therefore, the improved reliability of Ensemble modelling confirms the efficacy and suitability of the proposed approach for decision-makers in land management at local and regional scales.

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Acknowledgements

The authors would like to acknowledge the Centro Studi Rischi Geologici of the Cinque Terre National Park. The authors also thank Dr. Alessandro Novellino (British Geological Survey) for revising the text and for providing valuable advice. Finally, the authors thank Consorzio interUniversitario per la prevenzione dei Grandi Rischi (CUGRI) for providing technological support. In addition, the authors would like to thank the two anonymous reviewers for their valuable and insightful comments to improve the paper.

Funding

The Cinque Terre National Park and its Director, Eng. Patrizio Scarpellini, provided funding for this project and for logistic support during field operations.

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Authors and Affiliations

  1. Department of Earth, Environmental and Life Sciences, University of Genoa, Corso Europa, 26, 16132, Genoa, Italy

    Mariano Di Napoli, Andrea Cevasco, Marco Firpo & Giacomo Pepe

  2. Department of Earth, Environmental and Resources Sciences, Federico II University of Napoli, Complesso Universitario di Monte Sant’Angelo (Edificio L) Via Cinthia, 21, 80126, Napoli, Italy

    Francesco Carotenuto, Pierluigi Confuorto, Diego Di Martire, Emanuele Raso & Domenico Calcaterra

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  1. Mariano Di Napoli
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  2. Francesco Carotenuto
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Correspondence to Pierluigi Confuorto.

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Di Napoli, M., Carotenuto, F., Cevasco, A. et al. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides 17, 1897–1914 (2020). https://doi.org/10.1007/s10346-020-01392-9

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