Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China
Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China
复制标题
利用新的组合模型预测滑坡位移——以中国青龙滑坡为例
DOI：
10.1007/s11069-019-03595-3
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发表时间：
2019-04-01
期刊：
影响因子：
3.7
通讯作者：
Liu, Pan
中科院分区：
文献类型：
--
作者：
Wang, Weidong;Li, Jiaying;Liu, Pan
滑坡位移预测在滑坡预警中具有重要作用。为此目的提出了许多模型。然而，这些模型的预测结果的准确性往往在不同的条件下变化。对这些结果的合理评价和全面考虑仍然是一个科学挑战。提出了一种新的滑坡位移预测的综合组合模型。分别采用支持向量机模型、指数平滑模型和灰色模型（GM）（1，1）对位移进行了初步预测。通过对模型结果的综合和引入精度矩阵，对模型结果进行了综合评价。得到了评价工作中的最优权重。利用组合模型可以得到合理的预测结果。该方法已通过贵州省青龙滑坡的应用进行了验证。预测结果与现场实测结果的对比表明，该模型具有较好的预测精度。组合模型的均方根误差（RMSE）可降至1.4316（监测部位JCK 2），1.2623（监测部位JCK4），2.3758（监测部位JCK6），2.2704（监测点JCK 8）、1.4247（监测点JCK 11）和0.9449（监测点JCK 12），这远低于单个模型的RMSE。
Prediction on landslide displacement plays an important role in landslide early warning. Many models have been proposed for this purpose. However, the accuracy of the prediction results by these models often varies under different conditions. Rational evaluation and comprehensive consideration of these results still remain a scientific challenge. A new comprehensive combination model is proposed to predict the landslides displacement. The elementary displacement prediction is made by the support vector machine model, the exponential smoothing model, and the gray model (GM)(1,1). The results of the models are comprehensively evaluated by combining the results and introducing the accuracy matrix. The optimal weight in the evaluation work is obtained. A rational prediction result can be attained based on the so-called combination model. The proposed method has been tested by the application of Qinglong landslides in Guizhou Province, China. The comparison between the prediction results and in situ measurement shows that the prediction precision of the proposed model is satisfactory. The root-mean-square error (RMSE) of the combination model can be reduced to 1.4316 (monitoring site JCK2), 1.2623 (monitoring site JCK4), 2.3758 (monitoring site JCK6), 2.2704 (monitoring site JCK8), 1.4247 (monitoring site JCK11), and 0.9449 (monitoring site JCK12), which is much lower than the RMSE of the individual models.