Abstract: The Jurassic weathered bedrock aquifer in Northwest China is the main aquifer for filling water in coal mines. Many water inrush disasters in the coal mines are related to the aquifer. The inhomogeneity of the aquifer is de- termined by its features of loose structure,large porosity,high permeability and fractures. How to accurately evaluate and predict the water abundance of Jurassic weathered bedrock is a scientific challenge in mine water prevention. The south wing of the Ningtiaota coalfield was taken as an example to analyze the water abundance of weathered bedrock and its controlling factors. Four indexes of weathered bedrock including top-level index,lithologic association index, weathered index and core rate index were selected as the main indexes for water-richness prediction. In order to over- come the shortcomings of AHP which emphasizes expert experience in determining weights but does not consider the characteristics of measured data, and the entropy weight method which emphasizes objective data in determining weights without considering expert experience,a method of calculating the weight of water abundance prediction index by coupling improved AHP and entropy weight method using least square method was proposed. Based on the 172 borehole geological data and the 41 weathered bedrock pumping tests data in the south wing of the Ningtiaota coalfield,the zoning of water-richness has been predicted. Also,its results were compared and analyzed with the im- proved AHP,the entropy weight method and the coupling of the improved AHP and the entropy weight method. The re- sults show that the water abundance accuracy predicted quantitatively by the improved AHP weight determination method,the entropy weight determination method and the coupling method are 68. 29% ,70. 73% and 92. 68% re- spectively. The result indicates that the coupling of the improved AHP and the entropy weight method can be used to predict the water-richness of weathered bedrock accurately. The method also provides a new way for the evaluation and prediction of aquifer water abundance.