[1]陈芳,张志强,李扉*,等. 基于EEMD分解与BOA算法优化神经网络的密云水库大阁水文站径流预测[J].西北林学院学报,2021,36(6):188-194.[doi:10.3969/j.issn.1001-7461.2021.06.27]
 CHEN Fang,ZHANG Zhi-qiang,LI Fei*,et al. Run-off Prediction of Dage Hydrological Station of Miyun Reservoir Based on EEMD Decomposition and Neutral Network Optimized by BOA Algorithm[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2021,36(6):188-194.[doi:10.3969/j.issn.1001-7461.2021.06.27]
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 基于EEMD分解与BOA算法优化神经网络的密云水库大阁水文站径流预测()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第36卷
期数:
2021年第6期
页码:
188-194
栏目:
出版日期:
2021-11-30

文章信息/Info

Title:
 Run-off Prediction of Dage Hydrological Station of Miyun Reservoir Based on EEMD Decomposition and Neutral Network Optimized by BOA Algorithm
文章编号:
1001-7461(2021)06-0188-07
作者:
 陈芳1张志强2李扉1*孙恺琦1
 (1.北京林业大学 理学院,北京 100083;2.北京林业大学 水土保持学院,北京 100083)
Author(s):
 CHEN Fang1ZHANG Zhi-qiang2LI Fei1*SUN Kai-qi1
 (1.College of Science,Beijing Forestry University,Beijing 100083,China; 2.School of Soil and Water Conservation,Beijing Forestry University,Beijing 100083,China)
关键词:
 密云水库R/S分析BP神经网络EEMD分解蝴蝶算法径流预测
Keywords:
 Miyun Reservoir R/S analysis BP neural network EEMD decomposition butterfly algorithm runoff prediction
分类号:
S757
DOI:
10.3969/j.issn.1001-7461.2021.06.27
文献标志码:
A
摘要:
 利用R/S分析法研究密云水库潮河流域大阁水文站1969-2013年的径流数据的变化趋势,以BP神经网络为背景,EEMD分解为辅助,建立分解-重构-预测的组合模型对月径流序列进行预测,利用蝴蝶算法(BOA)优化组合模型,综合得到最优预测模型。结果表明,大阁站年、月径流序列均呈现下降趋势;对月径流序列预测,BPNN预报合格率为60.0%,不能用于预报作业,但可作为参考使用(MAE=0.406,RMSE=0.539,MAPE=0.349 7);引入BOA算法优化BP网络参数,得到EEMD-BOA-BP模型预报合格率为83.3%,可以用于预报作业(MAE=0.257,RMSE=0.347,MAPE=0.219 5)。通过EEMD分解得到分解-重构-预测组合模型对提高模型精度有一定的作用,同时在组合模型中引入优化算法能进一步提高模型精度。
Abstract:
 The R/S analysis method was used to study the change trend of runoff data from 1969 to 2013 at the Dage Hydrological Station in the Chaohe Basin of Miyun Reservoir.With the BP neural network as the background,EEMD decomposition was assisted to establish a combination model of decomposition-reconstruction-prediction to predict the monthly runoff sequence.The butterfly optimization algorithm (BOA) was adopted to optimize the combination model to comprehensively obtain the optimal prediction model.The results of the study showed that the annual and monthly runoff series at the Dage Station showed a downward trend; for monthly runoff series prediction,the BPNN forecast pass rate was 60.0%,which could not be used for forecasting operations,but could be used as a reference (MAE=0.406,RMSE=0.539,MAPE=0.349 7); BOA algorithm was introduced to optimize the BP network parameters,and the EEMD-BOA-BP model forecast qualification rate was 83.3%,which met the requirement for forecasting operations (MAE=0.257,RMSE=0.347,MAPE=0.219 5).The decomposition-reconstruction-prediction combination model obtained through EEMD decomposition had a certain effect on improving the accuracy of the model.The introduction of the optimization algorithms into the combined model could further improve the accuracy of the model.

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备注/Memo

备注/Memo:
 收稿日期:2020-11-09修回日期:2021-05-12
基金项目:中央高校基本科研业务费专项资金(2019ZY20);国家自然科学基金(31872711)。
作者简介:陈芳。研究方向:智能算法、数据处理。E-mail:chenfang@bjfu.edu.cn
*通信作者:李扉,博士,副教授。研究方向:数学挖掘、数据分析,粗糙集理论及应用。E-mail:feifei_1004@bjfu.edu.cn
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