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Runoff Prediction and Comparative Study by Combining ICEEMDAN and Multiple Intelligent Optimization Algorithms
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Qin-nan MAOa, b, Zhao LIUb, c, Jie LIa, b, Shu-min WANGa, b, Ting-hao ZHANGa, b
Water Resources and Power | 2023, 41(10) : 23 - 26
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Water Resources and Power | 2023, 41(10): 23-26
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Runoff Prediction and Comparative Study by Combining ICEEMDAN and Multiple Intelligent Optimization Algorithms
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Qin-nan MAOa, b, Zhao LIUb, c, Jie LIa, b, Shu-min WANGa, b, Ting-hao ZHANGa, b
Affiliations
  • a.School of Water and Environment, Chang’an University, Xi’an 710054, China
  • b.Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of Ministry of Education, Chang’an University, Xi’an 710054, China
  • c.Institute of Water and development, Chang’an University, Xi’an 710054, China
Published: 2023-10-25 doi: 10.20040/j.cnki.1000-7709.2023.20230042
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In order to improve the accuracy and reliability of runoff prediction, the advantages of EMD in dealing with non-stationary time series are introduced, and a BP neural network prediction model based improved adaptive noise complete set empirical mode decomposition (ICEEMDAN) and whale algorithm (WOA) optimization is established. Taking the inflow runoff prediction of Jinpen Reservoir in Heihe, Shaanxi Province as an example, a simulation model based on multiple intelligent optimization algorithms is established to predict the inflow runoff of the reservoir. At the same time, historical data of different time series, such as precipitation and runoff, are selected as input factors to compare the prediction ability and results of BP, WOA-BP, ICEEMDAN-BP and ICEEMDAN-WOA-BP models under the same input factor conditions. The results show that as far as the input sequence is concerned, the prediction effect of the model with precipitation as the input factor is better than that of the model with runoff as the input factor; For different algorithms, ICEEMDAN-WOA-BP model has good stability, Nash coefficient can reach 80%-90%, and the prediction accuracy is higher. The proposed ICEEMDAN-WOA-BP model can provide technical support for river runoff prediction, reservoir hydrological prediction and watershed water resources management.

runoff prediction  /  ICEEMDAN  /  whale optimization algorithm  /  BP neural network
Qin-nan MAO, Zhao LIU, Jie LI, Shu-min WANG, Ting-hao ZHANG. Runoff Prediction and Comparative Study by Combining ICEEMDAN and Multiple Intelligent Optimization Algorithms[J]. Water Resources and Power, 2023 , 41 (10) : 23 -26 . DOI: 10.20040/j.cnki.1000-7709.2023.20230042
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Year 2023 volume 41 Issue 10
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230042
  • Receive Date：2023-01-09
  • Online Date：2026-01-28
  • Published：2023-10-25
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History
  • Received：2023-01-09
  • Revised：2023-02-08
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Affiliations
    a.School of Water and Environment, Chang’an University, Xi’an 710054, China
    b.Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of Ministry of Education, Chang’an University, Xi’an 710054, China
    c.Institute of Water and development, Chang’an University, Xi’an 710054, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20230042
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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