JOURNAL OF NATURAL RESOURCES ›› 2014, Vol. 29 ›› Issue (5): 885-893.doi: 10.11849/zrzyxb.2014.05.016

• Resources Research Methods • Previous Articles     Next Articles

Application Research on Combined Models Based on Wavelet Analysis in Prediction of Daily Runoff

WANG Xiu-jie, FENG Gui-min, GENG Qing-zhu   

  1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
  • Received:2013-01-04 Revised:2013-08-29 Online:2014-05-20 Published:2014-05-20
  • About author:10.11849/zrzyxb.2014.05.016

Abstract: In the process of development and utilization of water resources, real-time and accurate daily runoff forecast plays an important role in the fields such as disaster reduction, flood control and other aspects. The process of the daily runoff time series is jammed increasingly by human activities. By the multi-resolution performance of wavelet analyis, the daily runoff time series are decomposed into the low frequency and the high frequency parts with which autogressive models are built seperately. These models are combined to predict separately the daily runoff time series of Toudaoguai station and Huayuankou station along the Yellow River in three periods of from 1966 to 1968, from 1969 to 1986 and from1987 to 2005. Compared with the single model gained by the original daily runoff time series, the prediction precision of the combined models based on wavelet analysis is increased obviously. The prediction precisions of three periods are consistent basically. Their prediction pass rates are more than 90% and predicative values can be used in practice. But the prediction precisions of the single model vary largely in three periods. With the aggravation of the human activities, the prediction errors increase accordingly. The prediction pass rates of Huayuankou station don't meet 85.0% in two periods from 1969 to 1986 and from 1987 to 2005. Their predicative results can only be referenced. So it is thought that the combined model based on wavelet analysis is more anti-jamming and has more significant superiority in runoff prediction.

CLC Number: 

  • P333