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.
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Acknowledgements
The study was financially supported by National Natural Science Foundation of China (Grant No. 51478483, W. Wang; Grant No. 41702310, Z. Han); the National Key R&D Program of China (Grant No. 2018YFC1505401, Z. Han); and the Natural Science Foundation of Hunan (Grant No. 2018JJ3644, Z. Han); and the National Basic Research Program of China (Grant No. 2011CB 710601, L. Fang). These financial supports are gratefully acknowledged.
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Wang, W., Li, J., Qu, X. et al. Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China. Nat Hazards 96, 1121–1139 (2019). https://doi.org/10.1007/s11069-019-03595-3
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DOI: https://doi.org/10.1007/s11069-019-03595-3