Abstract: China is the country with the largest coal production and consumption in the world.However,due to the complex hydrogeological conditions,water inrush accidents occur frequently in the coal seam roof and floor during coal mining,causing serious economic losses and casualties.Identifying the source of water hazards rapidly and accurately is the key step in mine water inrush prevention and control.Based on the measured data of 67 water samples from Zhaogezhuang mine in Kailuan,the ion concentrations of six ions,including Na+,Ca2+,Mg2+,Cl-,SO2-4 and HCO-3,were taken as input items,and the type of water inrush source was taken as output item.The WOA-ELM discriminant model was developed by using the Whale Optimization Algorithm (WOA) to improve ELM for achieving the water inrush source discrimination.The results show that the single extreme learning machine has the disadvantage of poor stability,and the whale algorithm is used to iteratively optimize its weight and threshold values.The whale optimization algorithm using three methods of ring encirclement,air curtain attack,and random search is used to search for optimal parameters,with fast convergence speed and strong global search ability.A mathematical model is established based on the predation behavior of humpback whales.Due to the uncertainty of prey position (water inrush),the WOA algorithm first assumes that the current best candidate solution is the target prey position or the closest position to the prey,and then randomly generates the vector A and the probability p to determine the way that the whales update the position.When |A|>1,random search prey is chosen.When the |A|