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Prediction of maximal water bursting discharge from coal seam floor based on multiple nonlinear regression analysis

  • ISMSSE 2018
  • Published:
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Abstract

Aimed at the influences of various complicated factors for water inrush from a coal seam floor during coal seam mining, the prediction of the maximal water bursting discharge from a coal seam floor is regarded as a problem of pattern identification with nonlinear, high dimensions, and finite samples. Based on information fusion theory and regarding the prediction of maximal water bursting discharge from a coal seam floor as a process of multisource information fusion and state estimation, an evaluation system of the main controlling factors for predicting the maximal water bursting discharge from a coal seam floor was established. An evaluation model for forecasting the maximal water bursting discharge from a coal seam floor was also structured according to multiple nonlinear regression theory. Considering the major coal mines of Xinwen coalfield as the research background, six factors are selected as the basic discriminant factors, i.e., aquifer thickness, unit water inflow, water pressure, aquiclude thickness, fault influencing factor, and depth of a coal seam floor destroyed by rock pressure. Deng’s grey relational theory was used to analyze the correlation degrees between each main controlling factor and the maximal water bursting discharge from a coal seam floor. A structural equation model was adopted to reveal the internal connecting links among the key influencing factors and determine the weights of each main controlling factor for predicting the maximal water bursting discharge from a coal seam floor. An SPSS scatter plot and MATLAB functional programming were applied to fit the correlativity curves between each main controlling factor and the maximal water bursting discharge from a coal seam floor in the Xinwen Mining Area. The optimal unitary nonlinear regression models between the maximal water bursting discharge from the coal seam floor and each main control factor were established. A multiple nonlinear regression–modified model for predicting the maximal water bursting discharge from the coal seam floor was acquired using a multiple nonlinear regression analysis with the combined weights of each main control factor. Compared with the bivariant multiple regression equation and the measured data, the results showed that the average values of forecasting errors predicted by the multiple nonlinear regression–modified model and bivariant multiple regression equation are 10.53% and 16.76%, respectively, which indicates that the multiple nonlinear regression–modified model and the bivariant multiple regression equation have higher prediction accuracy and relatively smaller error ranges, with comparatively better application value in the prediction of maximal water bursting discharge from a coal seam floor.

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Acknowledgments

The authors thank all the experts for their careful work and thoughtful suggestions.

Funding

This work is financially supported by the National Natural Science Foundation of China (41572244, 51804184, 41807283), the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (2017RCJJ033), and the Taishan Scholars Construction Projects.

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Authors and Affiliations

  1. College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Xingyue Qu, Mei Qiu, Jinhui Liu, Zhichao Niu & Xiangsheng Wu

  2. Shandong Provincial Key Laboratory of Depositional Mineralization and Sedimentary Minerals, Qingdao, 266590, China

    Xingyue Qu & Mei Qiu

Authors
  1. Xingyue Qu
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  2. Mei Qiu
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  3. Jinhui Liu
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  4. Zhichao Niu
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  5. Xiangsheng Wu
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Corresponding author

Correspondence to Mei Qiu.

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This article is part of the Topical Collection on Mine Safety Science and Engineering

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Qu, X., Qiu, M., Liu, J. et al. Prediction of maximal water bursting discharge from coal seam floor based on multiple nonlinear regression analysis. Arab J Geosci 12, 567 (2019). https://doi.org/10.1007/s12517-019-4748-7

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