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

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.