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中文摘要：
滑坡灾害易发性预测是滑坡监测、预警与评估的关键技术。如何有效地选取评价因子和构建预测模型是滑坡灾害定量预测研究中的难题。本文以三峡库区长江干流岸坡作为研究区，通过地形、地质和遥感等多源数据融合，提取滑坡孕灾环境和诱发因素的信息作为评价因子。在此基础上，针对滑坡灾害的非线性和不确定性特征，采用粒子群算法对支持向量机模型参数进行全局寻优，构建粒子群算法（particle swarm optimization，PSO）一支持向量机（support vector machine，SVM）模型，定量预测滑坡易发性。最后通过分类精度比较分析基于格网单元和对象单元的滑坡易发性预测精度，结果表明，基于对象单元的PSO-SVM预测精度较高，其曲线下面积为0．8415，Kappa系数为0．8490，预测结果与野外实际调查情况较为一致，可为三峡库区滑坡防灾减灾工作提供参考。
英文摘要：
Landslide susceptibility prediction is the key technology in landslide monitoring, early warning, and assessment. The core problem in quantitative prediction of landslide hazards is the effective selection of conditioning factors and prediction models. In this paper, the Three Gorges Reservoir area was selected as a case study to predict landslide susceptibility. First,, key landslide-related factors were selected as input variables using topographic, geological, and remote sensing data. Secondly, according to the nonlinear and uncertainty characteristics of landslides, a PSO-SVM model was trained and used to assess landslide susceptibility. Finally, the prediction results of grid- and object-based prediction models were validated by comparing them with known landslides using the classification accuracy. The results show that object-based PSO-SVM possesses high prediction accuracy with the area under curve of 0. 841 5 and a Kappa coefficient of 0. 849 0. These experimental results are consistent with field investigations and can provide a reference for landslide prevention and reduction in the Three Gorges, China.
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