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Enhancing the reliability of landslide early warning systems by machine learning
Landslides ( IF 6.7 ) Pub Date : 2020-07-02 , DOI: 10.1007/s10346-020-01453-z
Hemalatha Thirugnanam , Maneesha Vinodini Ramesh , Venkat P. Rangan

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.

中文翻译:

通过机器学习提高滑坡预警系统的可靠性

本文提交了一份关于有效采用机器学习算法来提高降雨诱发滑坡可靠性的报告。已经介绍了设计可靠的滑坡预警系统 (LEWS) 所涉及的挑战以及克服这些挑战的数据驱动背景。LEWS 的运行通过五个主要组成部分 (i) 数据收集,(ii) 数据传输,(iii) 建模、分析和预测,(iv) 警告和 (v) 响应来解释。LEWS 的这些主要组件中的任何一个发生故障都将中断 LEWS 的操作链,并审查每个组件故障的后续后果。还介绍了对全球十几个地点的 12 个 LEWS 部署中纳入的可靠性措施的分析得出的推论。根据 12 LEWS 的调查和实际经验,我们确定需要一种替代解决方案来确保 LEWS 的可靠性,尤其是在警告至关重要但数据可用性受到限制的灾难场景中。我们认识到机器学习算法可以提供替代解决方案,在本文中,我们讨论了两种机器学习方法临近预报和预测以提高可靠性。这两种算法都利用滑坡监测参数的历史数据来了解导致滑坡发生率的斜坡变化。所学知识用于根据实时滑坡监测参数对边坡的实时和未来状况进行临近预报和预测。在保证可靠性方面,(i) 如果 LEWS 的数据收集组件或数据传输组件出现故障,则临近预报算法提供了替代解决方案。(ii) 预测算法为预警提供了额外的提前期,解决了预警过程中提前期少的问题。突破性在于,即使由于各种原因无法获得实时滑坡监测参数,这些算法也以最少的降雨预报信息输入进行临近预报和预报,从而恢复了 LEWS 中断的运行链。
更新日期:2020-07-02
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