Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm

Qingbiao Guo, Boqing Qiao (), Yingming Yang and Junting Guo ()
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Qingbiao Guo: State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 100000, China
Boqing Qiao: School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China
Yingming Yang: State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 100000, China
Junting Guo: State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 100000, China

Energies, 2024, vol. 17, issue 5, 1-16

Abstract: Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q , tangent of the main influence angle tanβ , horizontal movement coefficient b , and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value’s fitted root mean square error and horizontal movement value’s fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.

Keywords: mining subsidence; whale optimization algorithm; Sobol sequence; Lévy flight; probability integral method; parameter inversion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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