Chaotic Dynamic Characteristics and Integrated Prediction of Runoff in the Upper Reaches of Yangtze River

ZHOU Jian-zhong; PENG Tian

doi:10.11988/ckyyb.20180619

PDF(5267 KB)

Journal of Changjiang River Scientific Research Institute ›› 2018, Vol. 35 ›› Issue (10) : 1-9. DOI: 10.11988/ckyyb.20180619

SPECIAL CONTRIBUTION

Chaotic Dynamic Characteristics and Integrated Prediction of Runoff in the Upper Reaches of Yangtze River

ZHOU Jian-zhong^1,2, PENG Tian^1,2

Author information +

History +

Abstract

In view of the strong nonlinearity and non-stationarity of monthly runoff in the upper reaches of Yangtze River, a hybrid model integrating the chaos theory and an ensemble AdaBoost.RT extreme learning machine is proposed for monthly runoff analysis and prediction. Firstly, the chaotic characteristics of monthly runoff in watershed were researched and revealed based on parameter identification of the runoff system. The optimal delay time and embedding dimension of the monthly runoff time series are deduced. Secondly, with the time series of the reconstructed phase space matrix as input variables, an improved AdaBoost. RT algorithm based on self-adaptive dynamic threshold was incorporated to improve the performance of extreme learning machine. Finally, the optimal chaotic ensemble learning model for monthly runoff prediction was obtained. Results showed that the proposed model could evidently improve the generalization and stability of single extreme learning machine model, and thus achieve better prediction performance.

Key words

runoff forecasting / upper reaches of Yangtze River / chaotic dynamic characteristics / phase space reconstruction / extreme learning machine / integrated prediction

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHOU Jian-zhong, PENG Tian. Chaotic Dynamic Characteristics and Integrated Prediction of Runoff in the Upper Reaches of Yangtze River[J]. Journal of Changjiang River Scientific Research Institute. 2018, 35(10): 1-9 https://doi.org/10.11988/ckyyb.20180619

References

[1] 朱双, 周建中, 孟长青,等. 基于灰色关联分析的模糊支持向量机方法在径流预报中的应用研究[J]. 水力发电学报, 2015, 34(6): 1-6.
[2] HENSE A. On the Possible Existence of a Strange Attractor for the Southern Oscillation[J]. Beitr. Physical Atmosphere,1987, 60(1):34-47.
[3] HU Z, ZHANG C, LUO G, et al. Characterizing Cross-scale Chaotic Behaviors of the Runoff Time Series in an Inland River of Central Asia[J]. Quaternary International, 2013, 311(9): 132-139.
[4] 王秀杰, 练继建, 费守明. 基于小波消噪的混沌神经网络径流预报模型[J]. 水力发电学报, 2008, 27(5): 37-40.
[5] 于国荣, 夏自强. 混沌时间序列支持向量机模型及其在径流预测中应用[J]. 水科学进展, 2008, 19(1): 116-122.
[6] 郭晓亮, 王国利, 梁国华,等. 径流混沌时间序列的模糊支持向量机预测模型研究及其应用[J]. 水力发电学报, 2010, 29(3): 51-55.
[7] HONG M, WANG D, WANG Y, et al. Mid-and Long-term Runoff Predictions by an Improved phase-space Reconstruction Model[J]. Environmental Research, 2016, 148:560-573.
[8] TAORMINA R, CHAU K W. Data-driven Input Variable Selection for Rainfall-runoff Modeling Using Binary-coded Particle Swarm Optimization and Extreme Learning Machines[J]. Journal of Hydrology, 2015, 529:1617-1632.
[9] PENG T, ZHOU J, ZHANG C, et al. Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks[J]. Water, 2017, 9(6): 406.
[10]马细霞, 穆浩泽. 基于混沌分析的月径流序列耦合预测模型及其应用[J]. 水力发电学报, 2010, 29(6): 41-46.
[11]周建中, 张娟娟, 郭俊,等.基于小波消噪的混沌神经网络月径流预报模型[J]. 水资源研究, 2012, 1(3): 65-71.
[12]OUYANG Q, LU W, XIN X, et al. Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction[J]. Water Resources Management, 2016, 30(7): 2311-2325.
[13]孙惠子, 粟晓玲, 昝大为. 基于最优加权组合模型的枯季径流预测研究[J]. 西北农林科技大学学报(自然科学版), 2011, 39(11): 201-208.
[14]肖洁, 罗军刚, 解建仓,等. 变权组合预测模型在洪水预报中的应用[J]. 西北农林科技大学学报(自然科学版), 2013, 41(2): 215-221.
[15]FREUND Y, SCHAPIRE R E. Experiments with a New Boosting Algorithm[C]∥Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy. July 3-6,1996:148-156.
[16]HUANG G B, ZHU Q Y, SIEW C K. Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks[C]∥Proceedings of the IEEE International Joint Conference on Neural Networks, Budapest, Hungary. July 25-29,2004:985-990.
[17]PACKARD N H, CRUTCHFIELD J P, FARMER J D, et al. Geometry from a Time Series[J]. Physical Review Letters, 1980, 45(9): 712.
[18]TAKENS F. Detecting Strange Attractors in Turbulence[M]. Heidelberg: Springer, 1981.
[19]SOLOMATINE D P, SHRESTHA D L. AdaBoost R T: A Boosting Algorithm for Regression Problems[C]∥Proceedings of the IEEE International Joint Conference on Neural Networks,Budapest, Hungary. July 25-29, 2004:1163-1168 .
[20]SHRESTHA D L, SOLOMATINE D P. Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression[J]. Neural Computation, 2006, 18(7): 1678-1710.
[21]TIAN H X, MAO Z Z. An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace[J]. IEEE Transactions on Automation Science & Engineering, 2010, 7(1):73-80.