基于混沌优化BP神经网络的江河涌潮短期预报模型

为提高江潮潮时预报的准确性,针对经验模型和传统神经网络模型预测精度较差的局限性,本文采用基于相空间重构技术的BP神经网络模型预报江潮潮时。该模型首先对隔日的到潮时差序列进行混沌特性分析,然后利用重构相空间来确定BP神经网络的输入结构。该模型给出了到潮时差序列可能的误差预测,修正最终预报结果。通过对钱塘江四个观测站潮时预测,四个站点潮时统计的RMSE值平均减少了83.9%,表明该模型可靠且具有较高的预报精度。

Short-term prediction model of river tidal bores based on chaos optimization algorithms and BP neural networks

To improve the accuracy of tidal prediction, this paper presents a time series model using artificial neural network combined with chaos theory, and this model has been developed to overcome the limitation of empirical model and traditional neural network model. It determines the existence of chaotic behaviors in the data series of every-other-day difference in tidal time; then, phase-space reconstruction for the error series of empirical model is applied to neural network inputs. The model can give a prediction of errors that is useful for modifying or updating the final results. Prediction of the tidal times during one month at four tide observation stations on the Qiantang River shows that the model reduces the root mean square error (RMSE) by 83.9% and has accuracy higher than the traditional model.