Citation

He H, Quan G, Zhu H, Li W, Xing R, Zhao Y. Sci. Rep. 2022; 12(1): e18723.

Copyright

(Copyright © 2022, Nature Publishing Group)

DOI

10.1038/s41598-022-23567-1

PMID

36333392

Abstract

The prediction of possibility and risk classification of collapse is an important issue in the process of highway construction in mountain area. Based on the principle of rough set and support vector machine, a landslide hazard prediction model was established. First of all, according to field investigation, an evaluation index system and a sample set of evaluation index data were established, the rough set decision table was constructed by preprocessing the original data based on the function classification of standard evaluation index, and then, the influence indexes of the collapse activity were reduced by rough set theory, and the main 9 indexes affecting the collapse activity as the key discriminant factors of support vector machine model, namely slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock were extracted. Then, taking the data of 13 post earthquake collapses in Yingxiu-Wolong highway of Hanchuan County measured by the authors in the field as training samples, the optimal model parameters were analyzed and calculated. When the penalty parameter [Formula: see text] is 8 and the kernel parameter [Formula: see text] is 0.5, the correct rate of cross-validation is 100%, and the model is optimal. At last, 4 other landslide data were tested, the discriminant results of the test sample data were compared with the results obtained by uncertainty measure and distance discriminant analysis. The results show that the discriminant results of the test sample data by RS-SVM were consistent with the results obtained by uncertainty measure and distance discriminant analysis, the accurate rate is 100%. The collapse hazard analysis model based on rough set and support vector machine can reduce the computation while ensuring the accuracy of evaluation, and better solve the small sample and nonlinear problems, can provide certain a good idea for collapse hazard evaluation in the future.

Language: en