Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China

Weidong Wang (), Jiaying Li, Xia Qu, Zheng Han () and Pan Liu
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Weidong Wang: Central South University
Jiaying Li: Central South University
Xia Qu: China Railway Siyuan Survey and Design Group Co.LTD
Zheng Han: Central South University
Pan Liu: Central South University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 96, issue 3, No 6, 1139 pages

Abstract: Abstract 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.

Keywords: Landslide; Displacement prediction; Combination model; SVM; Exponential smoothing model; GM(1; 1) (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11069-019-03595-3

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