摘要
针对城市景观水体水华产生过程存在复杂性、时变性、不确定性等特点,为解决水华预测中准确度不高、预测模型过于复杂等问题,将主成分分析降维能力与人工神经网络自学习能力相结合,提出PCA-RBF神经网络水华预测模型。主成分分析的结果将作为RBF神经网络的输入矩阵,并可对城市景观水体的主要污染物进行分析。结果显示,PCA-RBF神经网络对水质的预测精度为0.763,平均相对误差为21.83%,对水华的预测精度为92.3%,远高于一般的RBF神经网络模型。PCA-RBF网络的水华预测模型泛化能力强,网络预测精度较高,为水华的预测、预警提供了有效的手段,对城市景观水体的水华防治具有指导意义。
Formation process of water bloom in urban landscape waters is complicated,time⁃varied and uncertain.To overcome shortages of bloom predication,such as low accuracy and excessively complicated prediction model,the dimension reduction ability of principal component analysis was combined with the self⁃learning ability of artificial neural network,and the PCA⁃RBF neural network model for bloom predication was proposed in this paper.The principal analysis results were used as the input matrixof RBF neural network and the main pollutants of landscape waters in urban areas were analysed.The results showed that the water quality prediction accuracy of the PCA⁃RBF neural network was 0.763,with an average relative error of 21.83%and the bloom prediction accuracy of 92.3%,which were much greater than those of a general RBF neural network model.The PCA⁃RBF neural network has strong generalization ability of bloom prediction model and high prediction accuracy,providing effective means for the prediction and early warning of water bloom and having a good guiding significance to the prevention and control of water bloom in urban landscape water.
作者
谢如意
李伦
冯振鹏
艾庆华
XIE Ru-yi;LI Lun;FENG Zhen-peng;AI Qing-hua(WISDRI Urban Construction Engineering&Research Incorporation Limited,Wuhan 430077,China;China Metallurgical Group Corporation Sponge City Institute of Technology,Wuhan 430077,China)
出处
《四川环境》
2024年第5期51-56,共6页
Sichuan Environment
关键词
水华预测
主成分分析
人工神经网络
景观水体
Bloom prediction
principal component analysis
neural network
landscape water
