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Rough Fuzzy Inference Model and its Application in Multi-factor Medium and Long-term Hydrological Forecast

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  • Yong-Ying Zhu
  • Hui-Cheng Zhou

Abstract

This paper targets efforts to integrate rough set theory and the fuzzy inference technique into the multi-element medium and long-term hydrological forecast. Rough set theory is used to predigest the data and deal with the redundant inconsistent initial information table. Accordingly, the factors are reduced with the attribute significance concept. The minimal solution which is as fuzzy inference forecast pattern rule set in the model is achieved according to the principle of maximal attribute significance and combination significance as well as rules frequency. The model is applied to forecast annual runoff of Dahuofang Reservoir in China. The results indicate that the forecast precision is improved with rough set and the model can effectively reflect the non-linear relations between the runoff and factors and provide an effective and adaptable method to solve forecast problems related to complex factors selection and minimal inference rule set generation. Copyright Springer Science+Business Media B.V. 2009

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  • Yong-Ying Zhu & Hui-Cheng Zhou, 2009. "Rough Fuzzy Inference Model and its Application in Multi-factor Medium and Long-term Hydrological Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 493-507, February.
    • Handle: RePEc:spr:waterr:v:23:y:2009:i:3:p:493-507
    DOI: 10.1007/s11269-008-9285-1
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    References listed on IDEAS

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    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
    2. Si-Hui Dong & Hui-Cheng Zhou & Hai-Jun Xu, 2004. "A Forecast Model of Hydrologic Single Element Medium and Long-Period Based on Rough Set Theory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 483-495, October.
    3. Slobodan Simonovic & Lanhai Li, 2004. "Sensitivity of the Red River Basin Flood Protection System to Climate Variability and Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 89-110, April.
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    Cited by:

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    2. Ping-Feng Pai & Lan-Lin Li & Wei-Zhan Hung & Kuo-Ping Lin, 2014. "Using ADABOOST and Rough Set Theory for Predicting Debris Flow Disaster," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1143-1155, March.
    3. Qiang Zhang & Ben-De Wang & Bin He & Yong Peng & Ming-Lei Ren, 2011. "Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2683-2703, September.
    4. Gokmen Tayfur & Vijay Singh, 2011. "Predicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1253-1267, March.

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