The prediction and advanced warning of landslide hazards in large-scale areas must deal with a large amount of uncertainty, therefore a growing number of studies are using stochastic models to analyze the probability of landslide occurrences. In this study, we used a modified Thiessen’s polygon method to divide the research area into several rain gauge control areas, and divided the control areas into slope units reflecting the topographic characteristics to enhance the spatial resolution of a landslide probability model. We used a 2000–2015 long-term landslide inventory, daily rainfall, and effective accumulated rainfall to estimate the rainfall threshold that can trigger landslides. We then employed a Poisson probability model and historical rainfall data from 1987 to 2016 to calculate the exceedance probability that rainfall events will exceed the threshold value. We calculated the number of landslides occurring from the events when rainfall exceeds the threshold value in the slope units to estimate the probability that a landslide will occur in this situation. Lastly, we employed the concept of conditional probability by multiplying this probability with the exceedance probability of rainfall events exceeding the threshold value, which yielded the probability that a landslide will occur in each slope unit for one year. The results indicated the slope units with high probability that at least one rainfall event will exceed the threshold value at the same time that one landslide will occur within any one year are largely located in the southwestern part of the Taipei Water Source Domain, and the highest probability is 0.26. These slope units are located in parts of the study area with relatively weak lithology, high elevations, and steep slopes. Compared with probability models based solely on landslide inventories, our proposed landslide probability model, combined with a long-term landslide inventory and rainfall factors, can avoid problems resulting from an incomplete landslide inventory, and can also be used to estimate landslide occurrence probability based on future potential changes in rainfall.

中文翻译：

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基于长期滑坡清单和降雨因子的滑坡概率模型

大范围地区滑坡灾害的预测和预警必须处理大量的不确定性，因此越来越多的研究采用随机模型来分析滑坡发生的概率。本研究采用改进的泰森多边形法将研究区划分为若干雨量计控制区，并将控制区划分为反映地形特征的坡度单元，以提高滑坡概率模型的空间分辨率。我们使用 2000-2015 年的长期滑坡清单、日降雨量和有效累积降雨量来估计可能引发滑坡的降雨阈值。然后我们采用泊松概率模型和 1987 年至 2016 年的历史降雨数据来计算降雨事件超过阈值的超出概率。我们根据坡度单元中降雨量超过阈值的事件计算了滑坡发生的次数，以估计在这种情况下发生滑坡的概率。最后，我们采用条件概率的概念，将该概率乘以超过阈值的降雨事件超过概率，得出每个斜坡单元一年内发生滑坡的概率。结果表明，在任何一年内至少有一次降雨事件超过阈值的概率较高的斜坡单元主要位于台北水源域的西南部，并且最高概率为 0.26。这些边坡单元位于研究区岩性较弱、海拔高、坡度陡的部分地区。与仅基于滑坡清单的概率模型相比，我们提出的滑坡概率模型结合长期滑坡清单和降雨因素，可以避免滑坡清单不完整导致的问题，也可以用于估计滑坡发生概率。未来降雨的潜在变化。并且最高的概率是 0.26。这些边坡单元位于研究区岩性较弱、海拔高、坡度陡的部分地区。与仅基于滑坡清单的概率模型相比，我们提出的滑坡概率模型结合长期滑坡清单和降雨因素，可以避免滑坡清单不完整导致的问题，也可以用于估计滑坡发生概率。未来降雨的潜在变化。并且最高的概率是 0.26。这些边坡单元位于研究区岩性较弱、海拔高、坡度陡的部分地区。与仅基于滑坡清单的概率模型相比，我们提出的滑坡概率模型结合长期滑坡清单和降雨因素，可以避免滑坡清单不完整导致的问题，也可以用于估计滑坡发生概率。未来降雨的潜在变化。