Developing a grey prediction model with high nonlinear prediction accuracy is an important issue in grey system theory. A new grey prediction model was developed that was the first to combine the idea of twin support vector regression with Hausdorff derivative operator. The new model is a non-linear data-driven model. An improved salp swarm algorithm is used to determine parameters of the model. Two numerical examples show that the error of the new model is smaller than the existing grey prediction models and least square support vector machine model. Moreover, with the displacement, precipitation, reservoir level elevation, variation velocity of reservoir level elevation, and displacement velocity of the previous month as the input variables, the new model was successfully used to predict the displacement of a landslide in the real-world. The new model is a powerful tool for solving nonlinear prediction problems.

开发具有较高非线性预测精度的灰色预测模型是灰色系统理论中的重要课题。开发了一种新的灰色预测模型，该模型是第一个将双支持向量回归与Hausdorff导数算子结合在一起的模型。新模型是非线性数据驱动模型。改进的Salp群算法用于确定模型的参数。两个数值例子表明，新模型的误差小于现有的灰色预测模型和最小二乘支持向量机模型。此外，以位移，降水，水库水位高程，水库水位高程变化速度和前一个月的位移速度作为输入变量，该新模型成功地用于预测现实世界中滑坡的位移。