A Multilevel Recognition Model of Water Inrush Sources: A Case Study of the Zhaogezhuang Mining Area

被引：0

作者：

Gang Lin

Dong Jiang

Donglin Dong

Jingying Fu

Xiang Li

机构：

[1] Chinese Academy of Sciences,Institute of Geographical Sciences and Natural Resources Research

[2] University of Chinese Academy of Sciences,College of Resources and Environment

[3] Ministry of Natural Resources ,Key Laboratory of Carrying Capacity Assessment for Resource and Environment

[4] China University of Mining and Technology,College of Geoscience and Surveying Engineering

[5] Beijing (CUMTB),undefined

来源：

Mine Water and the Environment | 2021年 / 40卷

关键词：

Water inrush source identification; Hydrochemical analysis; Improved genetic algorithm; Extreme learning machine; Zhaogezhuang mine;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Discriminating water inrush sources efficiently and accurately is necessary to control water in coal mines. We combined the improved genetic algorithm (IGA) and extreme learning machine (ELM) methods and applied this new method to the Zhaogezhuang mining area. The IGA-ELM method effectively solved the complex non-linear problems encountered in identifying water sources and proved to have several advantages over conventional methodology. The IGA for the hill-climbing method was adopted to use the weights and thresholds of the ELM, which overcame the prematurity of the traditional genetic algorithm and the instability of the ELM model. Three types of water were identified in different aquifers of the Zhaogezhuang mining area: SO4-Ca in the Laotang water, SO4·HCO3-Ca in the Ordovician limestone water, and HCO3-Ca in the fractured sandstone roof of the no. 12 and 13 coal seams. The water sample recognition was 95% accurate, which proved that the water inrush source in the Zhaogezhuang mining area was accurately identified by the IGA-ELM model.

引用

页码：773 / 782

页数：9