基于PCA和BP神经网络的径流预测

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。

Runoff Forecast Based on Principal Component Analysis and BP Neural Network

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.