基于改进麻雀搜索算法和支持向量机的边坡稳定性

边坡失稳是由多种因素共同作用的结果，常规的数学模型难以准确预测。为提高边坡稳定性预测精度，采用多策略融合改进麻雀搜索算法（improved sparrow search algorithm，ISSA）优化支持向量机（support vector machine，SVM），进而建立边坡稳定性预测模型（ISSA-SVM模型）。将重度、粘聚力、内摩擦角、边坡角、边坡高、孔隙压力比6项因素作为输入特征，将边坡稳定性状态作为输出结果，进而预测边坡稳定性。选取国内外工程实例建立边坡数据库，将ISSA-SVM模型与SSA-SVM模型进行对比分析，通过灰色关联度分析法（grey relation analysis，GRA）进行敏感性分析。结果表明：ISSA-SVM模型预测精度更高、泛化能力更强，粘聚力和内摩擦角是对边坡稳定性最为敏感的因子。所提ISSA-SVM模型不仅能够准确地预测边坡稳定状态，还可以为其他领域相关问题提供参考。

Research on slope stability based on improved sparrow search algorithm and support vector machine

Slope instability is the result of a variety of factors, so conventional mathematical models are difficult to get accurate prediction results. To improve the accuracy of slope stability prediction, the improved sparrow search algorithm (ISSA) is used to optimize the support vector machine (SVM). Thus, a new slope stability prediction (ISSA-SVM) model is established. The gravity, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio are treated as input features, and the slope stability state is used as the output. Then, the prediction results of slope stability can be gotten. The domestic and foreign engineering examples are selected to establish a slope database, the ISSA-SVM model is compared with the SSA-SVM model, and the sensitivity analysis is performed using grey relation analysis (GRA). The results show that: the prediction accuracy of the ISSA-SVM model is higher, the generalization ability of the ISSA-SVM mode is stronger, and cohesion and internal friction angle are the most sensitive factors to slope stability. The proposed ISSA-SVM model can not only accurately predict the slope stability state, but also provide reference for the related problems in other areas.