A prediction method of karst cave scale based on the binary classification model of the Gaussian process

ZHANG Binghui; ZHANG Yan; WANG Wei; LIANG Jiahao

doi:10.11932/karst2020y04

Volume 39 Issue 2

Apr. 2020

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Article Navigation > CARSOLOGICA SINICA > 2020 > 39(2): 259-263

ZHANG Binghui, ZHANG Yan, WANG Wei, LIANG Jiahao. A prediction method of karst cave scale based on the binary classification model of the Gaussian process[J]. CARSOLOGICA SINICA, 2020, 39(2): 259-263. doi: 10.11932/karst2020y04

Citation:

ZHANG Binghui, ZHANG Yan, WANG Wei, LIANG Jiahao. A prediction method of karst cave scale based on the binary classification model of the Gaussian process[J]. CARSOLOGICA SINICA, 2020, 39(2): 259-263. doi: 10.11932/karst2020y04

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A prediction method of karst cave scale based on the binary classification model of the Gaussian process

doi: 10.11932/karst2020y04

1.
Guangxi Key Laboratory of New Energy and Building Energy Conservation, Guilin，Guangxi 541004, China/College of Civil Engineering and Architecture, Guilin University of Technology, Guilin，Guangxi 541004, China
2.
College of Civil Engineering, Tongji University, Shanghai 200092, China

Publish Date: 2020-04-25

Abstract

Abstract

A complex non-linear relationship exists between the scale of karst caves and its influencing factors. While the scale of karst caves can be predicted by pattern recognition based on influencing factors. A method based on the Gaussian process for the binary classification model (GPC) is proposed to predict the scale of karst caves. In this method, the complex nonlinear relationship between the scale of karst caves and influencing factors is established by learning a few samples. It gives probabilistic output identification for forecasting samples that only provide influencing factors. Research suggests that the proposed method not only has merits of small training samples, self-adaptive parameters determination and high recognition accuracy, but also can give the probabilistic credibility for prediction results. This method can provide a quantitative basis for effective prediction of the scale of karst caves in engineering practice, and has a good application prospect.
- scale of karst cave,
- prediction,
- Gaussian process,
- binary classification model,

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References

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