摘要
地下水位是衡量生态环境优劣和地下水资源的一个重要指标。因此预测地下水位对水资源的合理调度具有十分重要的意义。本文利用粒子群算法优化BP神经网络,建立地下水位预测模型。通过实例仿真,证明了该粒子群-BP神经网络模型的预测精度较好。
Groundwater level is an important index to evaluate the ecological environment and groundwater resources. Therefore, it is of great significance to predict the groundwater level for the rational allocation of water resources. This paper uses particle swarm optimization(pso) to optimize BP neural network and establish groundwater level prediction model. Simulation results show that the prediction accuracy of the pso-BP neural network model is good.
引文
[1]卓中文,王山东,杨松.基于BP神经网络的矿山地下水位预测研究[J].计算机与数字工程.2012,40(10).
[2]李慧,周维博,刘博洋,李娜,马聪.基于粒子群优化算法的RBF神经网络在泾惠渠灌区地下水位埋深预测中的应用[J].水电能源科学.2014,32:1-4.