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Enhancing the reliability of landslide early warning systems by machine learning

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Abstract

This paper submits a report on the effective adoption of machine learning algorithms for enhancing the reliability of rainfall-induced landslides. The challenges involved in the design of reliable landslide early warning systems (LEWS) and the data-driven context for overcoming these challenges have been presented. The operation of LEWS is explained using the chain of five major components (i) Data collection, (ii) Data transmission, (iii) Modelling, analysis and forecasting, (iv) Warning, and (v) Response. Failure of any of these major components of the LEWS will break the chain of operation of LEWS and the ensued consequences of each component failure are reviewed. Inferences drawn from the analysis of the reliability measures incorporated in 12 LEWS deployments across a dozen locations around the world are also presented. Based on the investigations from 12 LEWS and the real-world experience, we identified that an alternate solution is required for ensuring the reliability of LEWS, especially during disaster scenarios when warnings are crucial, but data availability is a constraint. We recognized that machine learning algorithms can provide an alternate solution and in this paper, we have discussed two machine learning approaches nowcasting and forecasting for enhancing the reliability. Both the algorithms employ historic data of the landslide monitoring parameters to learn the changes materializing in slope leading to landslide incidences. The learned knowledge is used to nowcast and forecast the real-time and future conditions of the slope from the real-time landslide monitoring parameters. In terms of ensuring reliability, (i) Nowcasting algorithm provides an alternate solution if either the Data collection component or Data transmission component of a LEWS fails. (ii) Forecasting algorithm provides extra lead-time for early warning and solves the problem of less lead-time during early warning process. The breakthrough is even when the real-time landslide monitoring parameters are not available for various reasons, these algorithms take the minimal input of rainfall forecast information for nowcasting and forecasting thus restoring the broken chain of operation of LEWS.

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Acknowledgements

The authors would like to express their immense gratitude to Sri. Mata Amritanandamayi Devi (AMMA), Chancellor, Amrita Vishwa Vidyapeetham, who gave us the motivation and inspiration to pursue this research work. We undertook this work following our recognition as the “World Center of Excellence in landslide disaster risk reduction”, conferred to us by the “IPL-International Programme on Landslides” in August 2017. The authors would also like to acknowledge the contributions of the entire landslide team in our research centre for their support in various aspects.

Funding

This work is partly funded by the Ministry of Earth Sciences (MoES), Government of India, under the project titled Advancing Integrated Wireless Sensor Networks for Real-time monitoring and detection of Disasters and partly funded by Amrita University.

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Authors and Affiliations

  1. Amrita Center for Wireless Networks & Applications, Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Hemalatha Thirugnanam, Maneesha Vinodini Ramesh & Venkat P. Rangan

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  1. Hemalatha Thirugnanam
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  2. Maneesha Vinodini Ramesh
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  3. Venkat P. Rangan
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Correspondence to Hemalatha Thirugnanam.

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Thirugnanam, H., Ramesh, M.V. & Rangan, V.P. Enhancing the reliability of landslide early warning systems by machine learning. Landslides 17, 2231–2246 (2020). https://doi.org/10.1007/s10346-020-01453-z

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