基于多重约束优化的滑坡变形组合预测研究

摘要

为实现滑坡变形的高精度预测,进而达到滑坡稳定性判断的目的,首先采用BP神经网络、支持向量机及GM(1,1)模型对滑坡变形进行传统的单项预测,且为提高单项预测精度,再采用遗传算法、粒子群算法及半参数法对各单项预测模型进行优化;其次基于多种组合指标,采用累加法和累乘法确定综合组合权值,实现对滑坡变形的组合优化预测.结果表明:组合预测结果的精度及稳定性均高于单项预测,而在综合权值的确定过程中,累乘法要优于累加法,且最优组合预测结果的相对误差平均值和标准差分别为0.81％和0.62％,具有较高的预测精度及稳定性,验证了预测思路对滑坡变形预测具有较好的适用性和有效性.%In order to realize the high precision forecast of landslide deformation and achieve the purpose of landslide stability evaluation,we applies the BP neural network,support vector machine and GM(1,1)model to the traditional single forecast of landslide deformation.To enhance the precision of the single forecast,the genetic algorithm,particle swarm algorithm and semi-parametric method were used to optimize the single forecast models.Based on multiple combination indexes,the comprehensive combined weights were determined through build-up method and tired multiplication to optimize combination forecast of landslide deformation.The results show that the precision and stability of the combination forecast results are higher than the single forecast.In the process of determining the comprehensive combined weights,tired multiplication is superior to build-up method.The average value and standard derivation of relative error of the optimal combination forecast results are 0.81％and 0.62 respectively,which proves that the forecast precision is high and stable and that the forecast method proposed in the paper is applicable and effective to landslide deformation forecast.