Abstract:  Observations of hydrologic series are always with tendency, periodicity, randomness and other characteristics.
Especially under large-scale conditions,there are many problems in the hydrologic series prediction,such as single construr
tion method and without consideration of the noise.Therefore,a hydrologic series prediction modcl(WD-RSPA model)is de
vcloped on the combination of wavclet de-noise(WD) method and rank and set pair analysis( RSPA) , which takes advantage
of multi-scale analysis and noise reduction in wavclet analysis and clear concept, simple calculation,and overcomes the sub-
jectivity to certain the standard of set elements in RSPA.The WD-RSPA model is applied to predict the annual runoff of
Huayuankou Station and the annual precipitation of Ghengzhou Station.The prediction results by WD-RSPA model are com-
pared with results of traditional RSPA modcl,AR model and BP neural network model, It indicates that,with appropriate de
noising wavclet function and pair dimension,WD-RSPA model can avoid the impact of noise efficicntly,the concept to estab-
lish WD-RSPA model is clear,the computation is simper and the accuracy of prediction results is higher. Consequcntly,the
applicability,dependability and advantage of WD-RSPA model is validated.