Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim of such systems is to forecast the increased probability of landslide occurrence in a given warning zone. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. This paper describes a new Excel user-friendly tool for the application of the EDuMaP method, originally proposed by (Calvello and Piciullo 2016). A description of indicators used for the performance evaluation of different Te-LEWS is provided, and the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the “SMART” warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need threshold refinement. A comparison of the performance of the SMART model with other models operating in different Te-LEWS has also been carried out, highlighting critical issues and positive aspects. Lastly, the SMART performance has been evaluated with both the EDuMaP and a standard 2 × 2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed assessment of the warning model, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval.

中文翻译：

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领土滑坡预警系统性能评估标准

滑坡预警系统 (LEWS) 可分为两类：领土系统和本地系统。领土滑坡预警系统 (Te-LEWS) 处理大范围内发生的几种滑坡：在市政/区域/国家范围内。此类系统的目的是预测给定预警区发生滑坡的可能性增加。此类系统的性能评估经常被忽视，并且仍然缺少标准化的程序。本文描述了一种新的 Excel 用户友好工具，用于应用 EDuMaP 方法，最初由 (Calvello and Piciullo 2016) 提出。提供了用于评估不同 Te-LEWS 性能的指标的描述，并且选择了最有用的指标并将其实施到工具中。EDuMaP 工具已被用于评估在意大利皮埃蒙特地区运行的“SMART”警告模型。分析突出显示了具有最高性能的警告区域和需要阈值细化的警告区域。还对 SMART 模型与在不同 Te-LEWS 中运行的其他模型的性能进行了比较，突出了关键问题和积极方面。最后，为了比较目的，已经使用 EDuMaP 和标准 2 × 2 列联表评估了 SMART 性能。结果突出表明，后一种方法可能导致对预警模型的评估不准确且不详细，因为它无法区分预警级别和时间间隔内可能发生的滑坡数量可变。