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A multi-dimensional statistical rainfall threshold for deep landslides based on groundwater recharge and support vector machines

Author

Listed:
  • A. Vallet

    (BRGM
    Université Bourgogne - Franche-Comté)

  • D. Varron

    (Université Bourgogne - Franche-Comté)

  • C. Bertrand

    (Université Bourgogne - Franche-Comté)

  • O. Fabbri

    (Université Bourgogne - Franche-Comté)

  • J. Mudry

    (Université Bourgogne - Franche-Comté)

Abstract

The rainfall threshold determination is widely used for estimating the minimum critical rainfall amount which may trigger slope failure. The aim of this study was to develop an objective approach for the determination of a statistical rainfall threshold of a deep-seated landslide. The determination is based on recharge estimation and a multi-dimensional rainfall threshold. This new method is compared with precipitation and with a conventional ‘two-dimensional’ rainfall threshold. The method is designed to be semiautomatic, enabling an eventual integration into a landslide warning system. The method consists in two independent parts: (i) unstable event identification based on displacement time series and (ii) multi-dimensional rainfall threshold determination based on support vector machines. The method produces very good results and constitutes an appropriate tool to define an objective and optimal rainfall threshold. In addition to shortened computation times, the non-necessity of pre-requisite hypotheses and a fully automatic implementation, the newly introduced multi-dimensional approach shows performances similar to the classical two-dimensional approach. This shows its relevance and its suitability to define a rainfall threshold. Lastly, this study shows that the recharge is a relevant parameter to be taken into account for deep-seated rainfall-induced landslides. Using the recharge rather than the precipitation significantly improves the delineation of a rainfall threshold separating stable and unstable events. The performance and accuracy of the multi-dimensional rainfall threshold developed for the Séchilienne landslide make it an appropriate method for integration into the present-day landslide warning system.

Suggested Citation

  • A. Vallet & D. Varron & C. Bertrand & O. Fabbri & J. Mudry, 2016. "A multi-dimensional statistical rainfall threshold for deep landslides based on groundwater recharge and support vector machines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 821-849, November.
    • Handle: RePEc:spr:nathaz:v:84:y:2016:i:2:d:10.1007_s11069-016-2453-3
    DOI: 10.1007/s11069-016-2453-3
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    Citations

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    Cited by:

    1. Francesco Fusco & Massimiliano Bordoni & Rita Tufano & Valerio Vivaldi & Claudia Meisina & Roberto Valentino & Marco Bittelli & Pantaleone De Vita, 2022. "Hydrological regimes in different slope environments and implications on rainfall thresholds triggering shallow landslides," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 907-939, October.
    2. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11485-11525, October.
    3. Jana Smolíková & Filip Hrbáček & Jan Blahůt & Jan Klimeš & Vít Vilímek & Juan Carlos Loaiza Usuga, 2021. "Analysis of the rainfall pattern triggering the Lemešná debris flow, Javorníky Range, the Czech Republic," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2353-2379, April.
    4. Massimiliano Bordoni & Valerio Vivaldi & Roberta Bonì & Simone Spanò & Mauro Tararbra & Luca Lanteri & Matteo Parnigoni & Alessandra Grossi & Silvia Figini & Claudia Meisina, 2023. "A methodology for the analysis of continuous time-series of automatic inclinometers for slow-moving landslides monitoring in Piemonte region, northern Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(2), pages 1115-1142, January.

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