基于GRA-MEA-BP耦合模型的城市需水预测研究

针对城市需水量影响因子多、BP神经网络收敛速度慢、精度低、易陷入局部最优等问题,提出灰色关联分析、思维进化算法、BP神经网络三者耦合的改进预测模型,利用灰色关联分析(GRA)筛选需水量主要影响因子,采用全局搜索能力极强的思维进化算法(MEA)优化BP神经网络的权值和阈值,从而构建GRA-MEA-BP耦合需水预测模型,同时建立BP神经网络模型作为对比。实例应用结果表明,GRA-MEA-BP耦合模型具有更高的预测精度和预测速度,可作为一种有效的需水预测模型。

Research on GRA-MEA-BP coupling model for water demand prediction

In order to solve the problem of multiple impact factors of urban water demand, slow convergence speed of BP neural network, low precision and easily falling into local optimum, this paper proposes an improved forecasting model based on the combination of grey relational analysis, mind evolutionary algorithm (MEA) and back propagation neural network (BPNN). grey relational analysis was adopted to select the main factors that influence the water requirement, and the weights and threshold values of BP neural network was optimized by using mind evolutionary algorithm (MEA) which has a strong global search ability to build the GRA-MEA-BP water demand forecast coupling model. And BP neural network model was established for comparison as well. The application results show that the GRA-MEA-BP coupling model has higher prediction accuracy and prediction speed. Thus, it can be used as an effective model for water demand forecasting.