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

被引：11

作者：

Lin, Gang ^{[1
,2
]}

Jiang, Dong ^{[1
,2
,3
]}

Dong, Donglin ^{[4
]}

Fu, Jingying ^{[1
,2
]}

Li, Xiang ^{[4
]}

机构：

[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China

[2] Univ Chinese Acad Sci, Coll Resources & Environm, 19A Yuquan Rd, Beijing 100049, Peoples R China

[3] Minist Nat Resources, Key Lab Carrying Capac Assessment Resource & Envi, 46 Fuchengmen Rd, Beijing 100812, Peoples R China

[4] China Univ Min & Technol Beijing CUMTB, Coll Geosci & Surveying Engn, Ding 11 Xueyuan Rd, Beijing 100083, Peoples R China

来源：

MINE WATER AND THE ENVIRONMENT | 2021年 / 40卷 / 03期

基金：

中国国家自然科学基金;

关键词：

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

D O I：

10.1007/s10230-021-00793-z

中图分类号：

TV21 [水资源调查与水利规划];

学科分类号：

081501 ;

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 center dot 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：10