Landslide dams are extremely dangerous because dammed rivers can inundate upstream areas with rising water levels and flood downstream areas after dam breaching. The longevity of landslide dams, which is uncertain, is of great significance for dam failure prevention and mitigation since it determines the time available to take mitigation measures. In this study, the full longevity of landslide dams is divided into three stages (infilling, overflowing and breaching) for better estimation. The influences of dam characteristic parameters (triggers, dam materials and geometric/hydrological parameters) on the full longevity of landslide dams (the period from landslide dam formation to the end of dam failure) as well as on each of the three stages are analysed based on the database. Based on eight dimensionless variables, regression models for estimating the full longevity of landslide dams are developed with a R 2 value of 0.781, and regression models for the three-stage longevity (the longevity as the sum of the periods of the three stages) by considering infilling, overflowing and breaching are established with a R 2 value of 0.938. It is found that the landslide dam longevity cannot be predicted by one or two influencing factors since it is affected by multiple factors. The relative importance of each control variable is evaluated based on sensitivity analysis: the trigger is the most significant variable in the breaching stage since it affects the size of dam particles, the water content and the inflow rate (e.g. the rainfall trigger results in a larger inflow rate); the lake volume coefficient is more significant in the overflowing stage because it indicates the potential volume of water eroding the dam; and the average annual discharge coefficient is the most important factor in the infilling stage because it controls the time to impound water. The longevity predicted by different models are compared. The models developed in this paper show better accuracy due to the consideration of more parameters based on more cases. In particular, the three-stage longevity regression model shows better accuracy than that of other models because it considers the particular influencing factors for each stage. Three case studies (the “10·10” Baige, Hsiaolin and Tangjiashan landslide dams) are presented to show the application of the regression models developed in this paper. The dam longevity can be predicted more precisely if the timely inflow rate can be estimated by site monitoring or multi-temporal remote sensing images and pre-event digital elevation model (DEM).

中文翻译：

---

滑坡坝寿命分析

山体滑坡大坝极其危险，因为被筑坝的河流会淹没上游水位上升的地区，并在大坝溃坝后淹没下游地区。滑坡坝的寿命是不确定的，它决定了采取缓解措施的可利用时间，因此对于大坝溃决的预防和缓解具有重要意义。在这项研究中，为了更好地估计滑坡坝的完整寿命，将其分为三个阶段（填充、溢流和破坏）。分析了大坝特征参数（触发器、大坝材料和几何/水文参数）对滑坡大坝全寿命（从滑坡大坝形成到溃坝结束的时期）以及三个阶段中的每一个阶段的影响。在数据库上。基于八个无量纲变量，建立了估计滑坡坝全寿命的回归模型，R 2 值为0.781，并建立了考虑充填、溢流和破坏的三阶段寿命（寿命为三个阶段周期的总和）回归模型R 2 值为 0.938。研究发现，滑坡坝寿命受多种因素影响，不能仅靠一两个影响因素来预测。基于敏感性分析评估每个控制变量的相对重要性：触发器是破坏阶段最显着的变量，因为它影响大坝颗粒的大小、含水量和入流量（例如降雨触发器导致更大的流入率）；湖泊容积系数在溢流阶段更为显着，因为它表明了可能侵蚀大坝的水量；而年均流量系数是充填阶段最重要的因素，因为它控制着蓄水时间。比较了不同模型预测的寿命。由于基于更多情况考虑了更多参数，本文开发的模型显示出更好的准确性。特别是三阶段寿命回归模型比其他模型显示出更好的准确性，因为它考虑了每个阶段的特定影响因素。三个案例研究（“10·10”白阁、小林和唐家山滑坡坝）展示了本文开发的回归模型的应用。