神经网络模型在水文模拟中的应用研究

Study on application of BP neural network model in hydrological simulation

针对传统BP神经网络模型存在收敛精度不高,引入小波函数对传统BP神经网络模型节点计算方法进行改进,并将改进的BP神经网络模型用于大洋河水文模拟研究中。研究结果表明:改进的BP神经网络模型可改变传统神经网络模型的收敛精度,在大洋河流域洪水尺度水文模拟中,相比于传统BP神经网络模型,模拟精度得到明显提高。

Because of the low convergence precision of traditional BP neural network model, the wavelet function is introduced to improve the node calculation method of traditional BP neural network model. The improved BP neural networks model is used in the Dayanghe hydrology simulation study. The results show that the improved model could change the convergence precision of the traditional model. Compared with the traditional model,the simulation accuracy has the significantly improve for the improved model in the flood simulation of Dayanghe river basin.