基于EEMD与ANN混合方法的水库月径流预测
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摘要
为了解决径流序列复杂的非稳态特征并提高径流的预报精度,采用EEMD-ANN组合方法构建径流预报模型,其中EEMD方法通过将非线性非稳态的水文序列分解为多组固有模态分量及趋势项,实现径流序列的稳态化,然后使用ANN方法分别进行预测,进而完成径流序列重构。以黄河龙羊峡水库为例,基于EEMD-ANN预报模型对入库径流量进行了预测,结果表明该方法可较精准地预测径流量。同时,通过对比分析发现,采用EEMD-ANN连续滚动预测月径流量在汛期的预报效果较好,而非汛期可采用同期预报的手段提高径流预报精度。
In order to improve the prediction accuracy, ensemble empirical mode decomposition(EEMD) and artificial neural network(ANN) hybrid model was proposed for monthly runoff forecasting, which considered runoff series complicated non-stationary characteristics. Firstly, in order to achieve steady state of runoff series, the non-linear and non-stationary runoff series was decomposed into several intrinsic mode functions(IMFs) and a trend series by using EEMD. Then, ANN model was established for different IMFs and the trend series respectively. Lastly, reconstructing forecast runoff sequence by superimposing all forecasting model. Taking Longyangxia(LYX) Reservoir of the Yellow River as an example, the inflow was predicted based on EEMD-ANN model. The results indicated that the hybrid method could accurately forecast the inflow of LYX Reservoir. At the same time, through comparison continuous and adaptive forecast and runoff series of same month forecast based on EEMD-ANN, it was found that the former method was better in flood season for forecasting monthly runoff, while runoff series of same month forecast method could be used to improve the accuracy of runoff prediction in dry season.
In order to improve the prediction accuracy, ensemble empirical mode decomposition(EEMD) and artificial neural network(ANN) hybrid model was proposed for monthly runoff forecasting, which considered runoff series complicated non-stationary characteristics. Firstly, in order to achieve steady state of runoff series, the non-linear and non-stationary runoff series was decomposed into several intrinsic mode functions(IMFs) and a trend series by using EEMD. Then, ANN model was established for different IMFs and the trend series respectively. Lastly, reconstructing forecast runoff sequence by superimposing all forecasting model. Taking Longyangxia(LYX) Reservoir of the Yellow River as an example, the inflow was predicted based on EEMD-ANN model. The results indicated that the hybrid method could accurately forecast the inflow of LYX Reservoir. At the same time, through comparison continuous and adaptive forecast and runoff series of same month forecast based on EEMD-ANN, it was found that the former method was better in flood season for forecasting monthly runoff, while runoff series of same month forecast method could be used to improve the accuracy of runoff prediction in dry season.
引文
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[2] 李佳,王黎,马光文,等.基于SPA-ANN耦合模型的年径流预测[J].水力发电学报,2009,28(1):41-44.
[3] SHIRI J,KISI O.Short-Term and Long-Term Streamflow Forecasting Using a Wavelet and Neuro-Fuzzy Conjunction Model[J].Journal of Hydrology,2010,394 (3-4):486-493.
[4] NAYAK P C,SUDHEER K P,RANGAN D M,et al.A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series[J].Journal of Hydrology,2004,291 (1-2):52-66.
[5] WANG W,JING D.Wavelet Network Model and Its Application to the Prediction of Hydrology[J].Nature & Science,2003,1(1):67-71.
[6] KISI O,CIMEN M.A Wavelet-Support Vector Machine Conjunction Model for Monthly Streamflow Forecasting[J].Journal of Hydrology,2011,399(1):132-140.
[7] SANG Y F,WANG Z,LIU C.Comparison of the MK Test and EMDMethod for Trend Identification in Hydrological Time Series[J].Journal of Hydrology,2014,510 (3),293-298.
[8] ZHAO X H,CHEN X.Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting[J].Water Resources Management,2015,29(8):2913-2926.
[9] 韩锐,董增川,罗赟,等.基于EEMD的黄河上游主要来水区年来水量预测[J].人民黄河,2017,39(8):10-14.
[10] 马超,姜璇.基于EEMD-ANN的水库年径流预测[J].水电能源科学,2016,34(8):32-35.
[11] HUANG N E,SHEN Z,LONG S R,et al,The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis[J].Proceedings:Mathematical,Physical and Engineering Sciences,1998,454 (1971):903-995.
[12] Huang N E,WU Z.A Review on Hilbert-Huang Transform:Method and Its Applications to Geophysical Studies[J].Reviews of Geophysics,2008,46 (2):RG2006.
[13] TIWARI A,KANUNGO P.Dynamic Load Balancing Algorithm for Scalable Heterogeneous Web Server Cluster with Content Awareness[R].Chennai:Trendz in Information Sciences & Computing (TISC),IEEE,2010:143-148.
[14] 李强,吴健,许正文,等.利用EMD方法提取太阳活动周期成分[J].空间科学学报,2007,27(1):1-6.
[15] SIMON H.Neural Network:A Comprehensive Foundation[M].London:Macmillan College Publishing,1994:71-80.
[16] HECHT-NIELSEN R.Theory of the Backpropagation Neural Network[J].Neural Networks,1988,1(1):445.
[17] TAN Q F,LEI X H,WANG X,et al.An Adaptive Middle and Long-Term Runoff Forecast Model Using EEMD-ANN Hybrid Approach[J].Journal of Hydrology,2018,567(12):767-780.