The purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

中文翻译：

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韩国春川滑坡预警系统的最佳降雨阈值和概率降雨条件

本研究的目的是建立滑坡预警系统 (LEWS) 的标准。我们通过推导累积事件降雨持续时间 (ED) 的最佳阈值并通过贝叶斯模型识别与滑坡发生的高概率相关的降雨变量的特征来实现这一点。我们使用韩国春川的降雨和滑坡数据建立了这些系统标准。强降雨是春川滑坡的主要原因；因此，确定引发山体滑坡的降雨条件至关重要。利用来自七个测量站的 1999 年至 2017 年的每小时降雨量数据来建立 ED 阈值和贝叶斯模型。我们使用了三个不同的降雨事件校准周期，分为 12、24、48、和 96 个非降雨小时来校准 ED 阈值。最后，通过比较列联表的结果和最大化检测概率（POD）得分和最小化错误检测概率（POFD）得分的技能得分来确定最优阈值。在 LEWS 中，通过将第一级视为“正常”，我们根据贝叶斯模型和 ED 阈值制定了后续的逐步警告级别。当降雨条件高于 ED 阈值时，我们建议使用第二个级别“观察”。然后，我们采用第三级“警告”和第四级“严重警告”，基于通过贝叶斯模型确定的滑坡发生概率，该模型考虑了包括滑坡降雨条件和滑坡降雨条件在内的多种因素。非滑坡和各种降雨变量，例如每小时最大降雨量和 3 天前降雨条件。作为模型适应性验证的结果，建议的警报级别预测了总共 98.2% 的滑坡发生在“严重警告”和“警告”级别。严重警告级别的误报率为0%，警告级别的误报率为47.4%。我们建议使用最佳 ED 阈值来预测当地何时可能发生滑坡。此外，我们基于贝叶斯模型提出了代表高滑坡概率的降雨变量范围，以设置适合当地特征的滑坡预警标准。2% 的滑坡发生在“严重警告”和“警告”级别，作为模型适应度验证的结果。严重警告级别的误报率为0%，警告级别的误报率为47.4%。我们建议使用最佳 ED 阈值来预测当地何时可能发生滑坡。此外，我们基于贝叶斯模型提出了代表高滑坡概率的降雨变量范围，以设置适合当地特征的滑坡预警标准。2% 的滑坡发生在“严重警告”和“警告”级别，作为模型适应度验证的结果。严重警告级别的误报率为0%，警告级别的误报率为47.4%。我们建议使用最佳 ED 阈值来预测当地何时可能发生滑坡。此外，我们基于贝叶斯模型提出了代表高滑坡概率的降雨变量范围，以设置适合当地特征的滑坡预警标准。