基于MFOA-SVR露天矿边坡变形量预测研究

为实现边坡危险性及时预警预报，以露天矿边坡变形量为研究对象，提出采用七项影响指标作为边坡位移变形量的响应参数，建立支持向量机回归预测模型(SVR)。引入修正的果蝇优化算法(MFOA)对模型参数进行优化，构建基于MFOA-SVR露天矿边坡变形量协同预测模型，并以实际监测数据进行模型仿真预测。结果表明:该模型平均绝对误差为0.9167mm，平均相对误差为4.2737%，较其他模型预测精度高，综合性能好，将其运用于露天矿边坡变形量预测研究具有较好的适用性和可靠性。

Research on forecasting of slope deformation in open-pit mine based on MFOA-SVR

In order to achieve the timely warning and forecasting of slope hazard, taking the slope deformation of open-pit mine as study object, it was proposed to establish the support vector machine regression model (SVR) by adopting 7 influence indexes as the response parameters of slope displacement and deformation. The modified fruit fly optimization algorithm (MFOA) was introduced to optimize the parameters of model. The collaborative forecasting model of slope deformation in open-pit mine based on MFOA-SVR was established, and the simulation and forecast of the model were conducted by practical monitoring data. The results showed that the mean absolute error of the model is 0.9167 mm, and the mean relative error is 4.2737%, which had higher precision and better comprehensive performance than other models. It has a good applicability and reliability when used in forecasting of slope deformation in open-pit mine.