地理科学进展

PROGRESS IN GEOGRAPHY ›› 2008, Vol. 27 ›› Issue (3): 161-165.doi: 10.11820/dlkxjz.2008.03.022

• Original Articles • Previous Articles     Next Articles

Study on Pr ediction Model Based on Segr egation and Aggr egation of Hydrologic Time Ser ies

ZHAO Changsen1,2,3, XIA Jun1, SHEN Bing2, ZHANG Huitong4, SUN Changlei5, HOU Zhiqiang6, Ya Likun7   

  1. 1. Institute of Geographical Science and Natural Resources Research,CAS, Beijing 100101, China;
    2. Xi'an University of Technology, Xi'an 710048, China;
    3. Department of water resource and environments, Sun Yat- sen University, Guangzhou 510275, China;
    4. Jinan Survey Bureau of Hydrology and Water Resources, Jinan 250000, China;
    5. Shandong Survey and Design Institute of Water Conservancy, Jinan 250013, China;
    6. Weifang Survey Bureau of Hydrology and Water Resources, Weifang 261000, China;
    7. Hotan Survey Bureau of Hydrology and Water Resources, Hotan 848000, China
  • Received:2008-01-01 Revised:2008-04-01 Online:2008-05-25 Published:2008-05-25

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Abstract:

To overcome the shortcomings in conventional forecast methods, a new Prediction Model based on Segregation and Aggregation of Hydrological Time Series (PMSAHTS) was put forward. Impacts of human activities on hydrological data sequences were firstly eliminated through segregation of trend and period signals in the data sequences. Secondly, the remaining random sequences were used as inputs to train BP Neutral Network, and then the trained network was used to predict random sequences in the future. Finally, the predicted random sequences were aggregated with the prediction results of trend and period terms. Thus the predicted hydrological sequences were obtained. To demonstrate this model, PMSAHTS was applied to predict the annual month- average evaporation in the Hotan Sub- project Area. It was shown by the results, among all comparisons of predicted values with measured ones, 62.5% of then have a prediction relative error less than 20%, which suggests that the PMSAHTS was qualified for hydrological prediction in practice.

Key words: periodic, PMSAHTS, prediction, random, trend