【摘要】 【目的】中国是世界上滑坡最频发的国家之一。建立一个可靠的全国范围内的滑坡易发性模型，以确定滑坡高易发地区、制定适当的防灾减灾策略、减少对人民生命财产损失显得非常必要。【方法】鉴于在我国这样大的区域上很难获得完全无偏的滑坡数据，本研究选择了坡度、坡向、剖面曲率、平面曲率、道路密度、河流密度、土壤湿度、岩性、土地利用和地质环境分区等10个影响因素作为驱动数据，设计了基于轻量级梯度提升机（LightGBM）并忽略滑坡数据不完整影响的模型方案一、基于LightGBM并排除与滑坡数据不完整相关因素的模型方案二和基于提升树混合效应模型（TBMM）并将描述滑坡数据不完整的变量（即土地利用和地质环境分区）作为随机效应项的模型方案三来分别评估滑坡灾害的易发性，以探索不完整滑坡数据对我国滑坡易发性建模的影响以及如何抵消这种偏差影响。【结果】研究表明，在大区域易发性建模的背景下；（1）尽管简单地忽略或排除与现有滑坡数据缺陷相关因素的模型方案具有更高的统计性能，但会导致预测的滑坡易发性结果在地貌上不合理；（2）应用混合效应模型可以有效降低滑坡数据的不完整性带来的偏差影响。【结论】本研究为滑坡数据不完整背景下的滑坡易发性制图提供了新思路，并有助于了解全国整体的滑坡灾害易发性状况，可协助决策者进行总体规划以降低灾害风险。更多还原

【Abstract】 [Objective] China is one of the countries with the most frequent landslide disasters in the world. It is necessary to establish a reliable landslide susceptibility model suitable for the whole country to determine the areas with high landslide hazards, formulate appropriate disaster prevention and reduction strategies,and reduce the loss of people’s lives and property. [Methods] Given the difficulty in obtaining completely unbiased landslide data in such a large area of China, this study selected 10 influencing factors such as slope, aspect,profile curvature, plan curvature, road density, river density, soil moisture, lithology, land use, and geological environment division as the driving data and designed Model Scheme 1(Based on LightGBM and ignoring the effects of incomplete landslide data), Model Scheme 2(Based on LightGBM and excluding factors associated with landslide incompleteness) and Model Scheme 3(Based on TBMM and including the variables describing landslide incompleteness, i.e. land use and geological environment division, as random effect terms) to assess landslide susceptibility separately to explore the impact of incomplete landslide data on the modelling of landslide susceptibility in China, the impact of incomplete landslide data on the modelling of landslide susceptibility in China,and the measure to counteract the effect of such bias. [Results] The results show that, in the context of large regional susceptibility modeling,(1) although the model schemes that simply ignore or exclude the factors associated with existing landslide data deficiencies have higher statistical performance, they will lead to geomorphically incoherent landslide susceptibility prediction results;(2) the mixed effects model can effectively reduce the bias impact caused by incomplete landslide data. [Conclusions] This study provides a new idea for landslide susceptibility mapping under the background of incomplete landslide data and contributes to assessing China’s overall mass movement susceptibility situation and assisting policymakers in master planning for risk mitigation.更多还原