文档介绍：第 卷 第 期 北 京 工 业 大 学 学 报
47 8 8 A 0254 - 0037(2021)08 - 0904 - 08
doi
: 10. 11936 / bjutxb2020120022
Prediction of Mine Water Inflow Based on CEEMD_GRU Model
LI Zhanli, XING Jinsha, JIN Hongmei, LI Hongan
(College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710600, China)
Abstract
: To improve the prediction accuracy of mine water inflow, a mine inflow prediction model
(CEEMD_GRU) based on the combination of complementary ensemble empirical mode decomposition
( CEEMD) and gated recurrent unit (GRU) neural network. First, the one-dimensional water inflow data
was decomposed into several intrinsic mode function ( IMF) components and a residual margin by
CEEMD algorithm. The fluctuation characteristics of the water inflow data at different time scales were
reflected by the intrinsic mode components while the trend characteristics of long-term changes of the data
was reflected by the residual margin. Then, the GRU neural network model was established for each
component, and the study of one-dimensional data was transformed into the study of the decomposed
multidimensional sub-components s