多隐层BP神经网络模型在径流预测中的应用

Application of Hidden Multilayer BP Neural Network Model in Runoff Prediction

基于人工神经网络基本原理和方法,构建多隐层BP神经网络径流预测模型,以新疆伊犁河雅马渡站径流预测为例进行分析,并构建常规单隐层BP以及RBF、GRNN神经网络模型作为对比分析模型,将各模型预测结果与文献[1]中的预测结果进行比较,结果表明:(1)多隐层BP神经网络径流预测模型泛化能力强,预测精度高,算法稳定,模型精度优于IEA-BP网络模型,表明研究建立的多隐层BP神经网络模型用于径流预测是合理可行的,是一种可以应用于水文径流预测预报的新方法。(2)RBF、GRNN神经网络径流预测模型预测精度高于常规单隐层BP网络模型,且RBF与GRNN神经网络模型具有收敛速度快、预测精度高、调整参数少,不易陷入局部极小值等优点,可以更快地预测网络,具有较大的计算优势。

Based on the basic principles of artificial neural networks and methods, a runoff forecasting model of hidden multilayer BP neural network was built, with runoff prediction at the Yamaha station on the Ili River as the study case. Otherwise, the conventional models based on single hidden layer BP, RBF and GRNN neural networks were structured for a comparative analysis. The results showed that （1） the runoff prediction model of hidden multilayer BP neural network has a strong generalization ability, prediction accuracy is high, and algorithm is stable. The model accuracy is better than the IEA-BP neural network model, which means as a new method to be used in runoff prediction, it is reasonable and feasible. （2） The prediction accuracy of RBF, GRNN neural network models is higher than the conventional single hidden layer BP network model because of advantages of fast convergence, high prediction accuracy, fewer parameters to be adjusted, and easy to fall into local minimum.