基于PCA和BP神经网络的径流预测

doi:10.7522/j.issn.1000-694X.2015.00039

摘要/Abstract

摘要： 径流预测为流域水资源的合理开发利用与统筹配置提供依据。运用多元线性回归、主成分回归、BP神经网络及主成分分析和BP神经网络相结合的方法，对新疆呼图壁河流域石门水文站2009-2011年各月径流量进行预测，并采用相关系数、确定性系数及均方根误差对各模型预测精度进行比较。结果表明：（1）神经网络等智能算法具有高速寻优的能力，对短时间尺度的月径流量的预测结果较好；（2）主成分回归等常规算法能充分反映出某地区径流的年际的稳定性，对全年径流总量的模拟精度较高；（3）主成分分析和BP神经网络相结合的方法，提高了神经网络的收敛速度，同时降低了局部极值的影响，优于简单的BP神经网络，适用于呼图壁河月径流量预测。

关键词: 主成分回归, 主成分分析, BP神经网络模型, 径流预测

Abstract: Runoff forecast provides a basis for the rational utilization and distribution of river basin water resources. This paper presents multiple linear regression, principal component regression, BP neural network model and a new model which combining the principal component analysis with the BP neural network. And those methods are used to predict the monthly runoff of the Hutubihe River in 2009-2011 collected at the Shimen Hydrological Station of Xinjiang. The prediction accuracy of each model compared by correlation coefficient, determination coefficient and root mean square error. The results show that: (1) Intelligent algorithm such as neural network has the ability of optimization in high speed, which gets better result on short time scales of monthly runoff forecast; (2) Conventional algorithm such as principal component regression can fully reflect the stability of the annual runoff in a given area, the simulation accuracy of total annual runoff is relatively higher than other methods; (3) The method combining the principal component analysis with the BP neural network which can improve the convergence speed of neural networks, while reduces the impact of local extremum, is better than simple BP neural network, and it is suitable for the monthly runoff forecast for the Hutubi River.

Key words: principal component regression, principal component analysis(PCA), BP neural network model, runoff forecast

中图分类号:

P338

聂敏, 刘志辉, 刘洋, 姚俊强. 基于PCA和BP神经网络的径流预测[J]. 中国沙漠, 2016, 36(4): 1144-1152.

Nie Min, Liu Zhihui, Liu Yang, Yao Junqiang. Runoff Forecast Based on Principal Component Analysis and BP Neural Network[J]. JOURNAL OF DESERT RESEARCH, 2016, 36(4): 1144-1152.

参考文献

[1] 黄锡荃,李惠明,金伯欣.水文学[M].北京:高等教育出版社,1993.
[2] 花蓓,熊伟,陈华. 模糊支持向量机在径流预测中的应用[J].武汉大学学报:工学版,2008,41(1):5-8.
[3] 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.
[4] 王伟,曹瑞仙,崔花瑞,等.新安江NAM和TOPMODEL模型在安阳河流域比较应用研究[J].河南水利与南水北调,2014,12(14):15-20.
[5] 袁喆,严登华,杨志勇,等.集合建模在径流模拟和预测中的应用[J].水利学报,2014,03:351-359.
[6] 蔡研聪,金昌杰,王安志,等.浑太森林流域径流过程模拟[J].应用生态学报,2013,10:2779-2786.
[7] 汤奇成,李秀云.干旱区的年径流分析及预测[J].中国沙漠,1986,6(2):1-7.
[8] 王秀杰,封桂敏,耿庆柱.小波分析组合模型在日径流预测中的应用研究[J].自然资源学报,2014,29(5):885-893.
[9] 杨洪.改进BP神经网络集成模型在径流预测中的应用[J].水资源与水工程学报,2014,25(3):213-219.
[10] 张楠,夏自强,江红.基于多因子量化指标的支持向量机径流预测[J].水利学报,2010,41(11):1318-1323.
[11] 蓝永超,康尔泗,徐中民,等.B-P神经网络在径流长期预测中的应用[J].中国沙漠,2001,21(1):97-100.
[12] 白继中,师彪,冯民权,等.基于自适应调整蚁群-RBF神经网络模型的中长期径流预测[J].自然资源学报,2011,26(6):1065-1074.
[13] 徐淑琴,刘琦,兰天洋,等.基于小波神经网络的穆棱河作物生育期径流预测[J].东北农业大学学报,2014,45(11):65-70.
[14] 周松林,茆美琴,苏建徽.基于主成分分析与人工神经网络的风电功率预测[J].电网技术,2011,35(9):128-132.
[15] 耿峻岭,高玲,陈建江,等.新疆呼图壁河流域水文特征分析[J].干旱区研究,2005,22(3):371-376.
[16] 白东明,李卫红,郝兴明,等.新疆呼图壁河流域径流时序变化特征[J].中国水土保持科学,2007,5(3):19-23.
[17] 徐浩杰,杨太保,柴绍豪.1961-2010年讨赖河山区径流变化特征及其驱动因素[J].中国沙漠,2014,34(3):878-884.
[18] 花婷,王训明,郎丽丽,等.甘肃省气温与降水变化趋势及其对主要流域径流量的影响[J].中国沙漠,2015,35(3):744-752.
[19] 王春娟,冯利华,陆小强.鄂尔多斯市水资源承载力的主成分分析[J].水资源与水工程学报,2012,23(1):77-80.
[20] 徐建华.计量地理学[M].北京:高等教育出版社,2006.
[21] 卓金武,魏永生,秦健,等.MATLAB在数学建模中的应用[M].北京:北京航空航天大学出版社,2011.
[22] 李国勇,杨丽娟.神经·模糊·预测控制及其MATLAB实现[M].北京:电子工业出版社,2013.
[23] Raman H,Sunilkumar N.Multivariate modelling of water resources time series using artificial neural network[J].Hydrological Sciences,1995,40(2):145-163
[24] Abudu S,Cui C L,King J P,et al.Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China[J].Water Science and Engineering,2010,3(3):269-281.