Abstract
Aiming to the fact that it is still difficult to reasonably forecasting the karstic collapse, the model based on GP machine learning is proposed for forecasting of karstic collapse. According to few learning samples, the nonlinear mapping relationship between karstic collapse and its influencing factors is established by GP model. The model was applied to a real engineering. The results of case study show that GP model is feasible, effective and simple to implement for forecasting of karstic collapse. Compared with artificial neural networks and support vector machine, it has attractive merits of self-adaptive parameters determination and excellent capacity for solving non-linear small samples problems.
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Zhang, Y., Su, G., Yan, L. (2011). Gaussian Process Machine Learning Model for Forecasting of Karstic Collapse. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23214-5_48
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DOI: https://doi.org/10.1007/978-3-642-23214-5_48
Publisher Name: Springer, Berlin, Heidelberg
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