基于FOA-GRNN模型的太湖水质预测研究

Research on taihu lake water quality prediction based on FOA-GRNN model

由于水环境较为复杂,传统水质预测方法难以建立理想的非线性系统。为了提高水质预测精度,提出一种利用果蝇算法(fruit fly optimization algorithm,FOA)改进广义神经网络(general regression neural network,GRNN)的水质预测模型。利用果蝇优化算法具有的全局寻优特性可以对关键参数进行寻优的特点,结合广义神经网络的高精度逼近能力,建立了FOA-GRNN水质预测模型。选取太湖水域0号观测站采集到的溶解氧、温度、总氮、总磷等4项数据,使用线性插值法、平滑法与归一法对数据进行预处理并实验仿真。仿真结果表明,FOA-GRNN的预测结果接近真实值,其4项预测指标的均方根误差分别为0.16483、0.25039、0.12659、0.11119,达到理想结果,具有稳定性强、精度高的优点,在水质预测方面有很大的实际应用价值。

Due to the complexity of the water system,it is difficult to establish an ideal nonlinear system with traditional water quality prediction methods.In order to improve the accuracy of water quality prediction,this paper proposes a water quality prediction model that uses the fruit fly optimization algorithm(FOA)to improve the general regression neural network(GRNN).Using the global optimization feature of the fruit fly optimization algorithm that can optimize the key parameters,combined with the high-precision approximation ability of the generalized neural network,the FOA-GRNN water quality prediction model is established.Four items of data including oxygen content,temperature,total nitrogen,and total phosphorus collected from observation station No.0 in Taihu Lake are selected,and the data are preprocessed and simulated by linear interpolation and normalization.The simulation results show that,compared with the GRNN model and the BP model,the prediction results of FOA-GRNN are closer to the true value.The root mean square errors of the four prediction indicators are 0.16483,0.25039,0.12659,and 0.11119,respectively,which are all lower than the GRNN model and the BP model have the advantages of strong stability and high accuracy,and have great practical application value in water quality prediction.