Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility

doi:10.11947/j.AGCS.2022.20220326

Abstract

Abstract: The analysis and evaluation of landslide disaster susceptibility is of great significance to the prevention and management of geological disasters. In view of the sample selection strategy and the unreasonable multi-feature mapping in single-kernel vector machine, this paper proposes the landslide susceptibility analysis and evaluation method of multiple kernel support vector machine (MKSVM) considering the sample optimization selection. To ensure sample balance and improve the plausibility of negative samples, using the relative frequency ratio (relative frequency, RF) comprehensively evaluate the importance degree of each state in the influence of landslide disaster susceptibility, the purpose is to realize the reasonable division of each evaluation factor state; Using the deterministic coefficient method (certainty factor, CF) calculates the sensitivity of each state of each evaluation factor, the weighted sum has obtained the landslide disaster susceptibility index of each grid cell, non-landslide disaster points consistent with the number of landslide disaster points were randomly selected in the very low and low landslide disaster prone index as the negative sample data. Then, multi-kernel learning is used to select the SVM optimal kernel function and to linear combine the optimal kernel functions in each feature space to avoid unreasonable mapping of a single kernel function, and it improve the classification accuracy and prediction accuracy of the model. Taking Xiangxi Tujia and Miao Autonomous Prefecture of Hunan province as the research area, MKSVM model of CF sample strategy, single-kernel SVM model of CF sample strategy, MKSVM model of random sample strategy and single-kernel SVM model of random sample strategy were compared analyzed from three aspects of landslide disaster prone zoning map, partition statistics and evaluation model accuracy. The results indicate that the subject operating characteristic curves of the four models (receiver operating characteristic, area under the ROC) (area under curve, AUC) were 0.859,0.809,0.798,0.766, the rationality and validity of the CF sample strategy and the reliability of the MKSVM model are verified.

Key words: landslide, susceptibility, certainty factor, multi-kernel, support vector machine

CLC Number:

P227

LIU Jiping, LIANG Enjie, XU Shenghua, LIU Mengmeng, WANG Yong, ZHANG Fuhao, LUO An. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2034-2045.

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