Prediction technology of mine water inflow based on entropy weight method and multiple nonlinear regression theory and its application

Autor: Bo Li, Huang Wu, Qiang Wu, Yifan Zeng, Xiaoming Guo
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Vol 10, Iss 1, Pp 1-13 (2024)
Druh dokumentu: article
ISSN: 2363-8419
2363-8427
DOI: 10.1007/s40948-024-00842-1
Popis: Abstract Mine water inflow is an important basis for the formulation of mining plans and the utilization of groundwater resources. The mine water inflow is the result of the combined influence of many factors. The weight value of the influencing factors is calculated by the entropy method, and the order of importance of the factors is: precipitation > mining depth > cumulative mined-out area > aquifer thickness > mining area > mining height. The optimal univariate nonlinear regression model of mine water inflow to each influencing factor is obtained by factor scatter analysis and Matlab function programming. On this basis, combined with the weight values of factors, a multivariate nonlinear regression prediction model of mine water inflow based on weighting is innovatively established, which overcomes the defect that the traditional water inflow prediction method that cannot reflect the relative importance differences of various influencing factors. The multivariate weighted nonlinear regression model is used to predict the mine water inflow of typical coal mines, and the prediction results are compared with the linear regression model and the measured value. The results show that the prediction model of mine water inflow based on weighted multivariate nonlinear regression is accurate higher, with higher practical application value.
Databáze: Directory of Open Access Journals
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