Abstract
The probability integral model is an important model to study the law of surface movement and deformation caused by coal mining. There are a certain number of undetermined parameters in the model, which need to be inverted by a certain mathematical method. In this paper, the objective function is established by the probability integral method, and the parameter inversion in the probability integral method is carried out by using the normal parameter inversion methods such as pattern search method, genetic algorithm and particle swarm optimization. By contrast the inversion results, the advantages and disadvantages of each algorithm in the stability of parameter results, resistance to gross errors and missing observation points are proved. The results show that PSM, GA and PSO are feasible to inverse parameters in the probability integral method, and the results are stable in the presence of gross errors and observation points. Compared with PSM and GA, the inversion accuracy of PSO is improved to some extent, which shows the advantages of PSO in parameter inversion of the probability integral method.
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The project was financially supported by the National Natural Science Foundation of China (no. 41404004).
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Yang, J., Yu, S., Liu, C. (2019). Comparison of the Inversion Methods for Probability Integral Parameters. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_41
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DOI: https://doi.org/10.1007/978-981-13-7025-0_41
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