采用机器学习方法在长阳土家族自治县研究区进行滑坡危险性评价，能够为地质灾害防治工作提供科学合理的依据。通过历史滑坡点选取研究区12个评价指标（平面曲率、地形起伏度、地表粗糙度、坡度、植被覆盖度、工程岩组、距断裂带距离、距水系距离、降雨量、土地利用类型、距房屋距离和距道路距离）相关性分析后均被选用。计算因子信息量，联合支持向量机（support vector machine, SVM）和梯度提升决策树（gradient boosting decision tree, GBDT）模型构建研究区的评价模型，将研究区危险性分为极高、高、中和低四个等级，生成危险性分区，并对评价模型进行评估。结果表明：极高危险区主要分布于研究区的西南部、中部和东部；I-SVM和I-GBDT模型预测的极高危险区、高危险区、中危险区和低危险区的分区占比分别为15.86%、21.29%、33.51%、28.68%和30.08%、7.41%、13.28%、49.22%，I-SVM和I-GBDT模型AUC（area under curve）值分别0.859、0.829。结果表明I-SVM模型的预测危险性分区结果更合理可靠。

Machine learning methods have been employed in the study area of Changyang Tujia Autonomous County for landslide hazard assessment, it could provide a scientific basis for geological disaster prevention and control efforts. Through the correlation analysis of 12 evaluation indicators (planar curvature, terrain undulation, surface roughness, slope, vegetation coverage, engineering lithology, distance to fault zone, distance to water system, rainfall, land use type, distance to buildings, and distance to roads) in the study area selected by historical landslide points, they were selected. And the evaluation model of the study area was constructed by calculating the information content of factors and integrate support vector machine (SVM) and gradient boosting decision tree (GBDT) models. The hazard of the study area was classified into four levels: extreme high, high, medium, and low, to generate hazard zoning. Subsequently, an assessment of the evaluation model was conducted. The results indicated that the very high hazard zone was mainly distributed in the southwest, central, and eastern parts of the research area. The distribution percentages of very high, high, medium, and low hazard zones predicted by the I-SVM and I-GBDT models were 15.86%, 21.29%, 33.51%, 28.68%, and 30.08%, 7.41%, 13.28%, 49.22%, respectively. The prediction of hazard zones by the I-SVM model aligned more closely with reality. The AUC values for the I-SVM and I-GBDT models were 0.859 and 0.829, respectively. The prediction of risk zones by the I-SVM model is deemed more reasonable and reliable.