基于相空间重构与神经网络耦合的岩溶泉流量预测模型

Forecast Model for Discharge of Karst Spring Based on the Coupling of Phase Space Reconstruction and Neural Network

岩溶水系统是受水文、地质、地形地貌、植被、人类活动等多种因素影响的非线性动力系统。本文利用重构相空间与神经网络,建立重构相空间与神经网络耦合的泉流量预测模型;通过对黑龙洞泉域泉流量的预测可知,所建立的耦合模型精度高。

The groundwater system of karst spring is a nonlinear and dynamic system influenced by factors such as hydrology, geology, landform, vegetation and human ＇ s activities. Based on the theory of phase space reconstruction and neural network, the forecast model for the discharge of karst spring is established from the coupling of phase space reconstruction and neural network. Through the application of the model to forecasting the discharge of Heilongdong spring system, it is shown that the precision of the forecast model is very high.