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

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.