Aiming at the problem of over training in hydrologic research using artificial neural networks (ANNs) and considering the composition of training samples,two new learning strategies were presented,i.e,optimized detecting method and weighted detecting method.The former chooses the optimal training data after comparing the validation sets.The latter endues the different sets with different weights in order to get a final validation outcome.The application of the two new methods at Zipingpu Hydrographic Station on Minjiang River upstream indicates that they can resolve the problem of over-training effectively.