IGOA-RBF模型在石门水库水质预测中的应用  

Application of IGOA-RBF model in water quality prediction of Shimen Reservoir

作　　者：孙晓蕾 SUN Xiao-lei

机构地区：[1]辽宁省营口水文局,辽宁营口115003

出　　处：《东北水利水电》2024年第6期26-27,41,71,共4页Water Resources & Hydropower of Northeast China

摘　　要：文中以石门水库为例,通过改进蚱蜢算法(IGOA)增加局部值的泛化性,并对RBF神经网络模型进行参数优化,构建了运行效率高、预测精确度高的IGOA-RBF的水质预测模型。结果表明,IGOA-RBF水质预测模型误差均较小,可为水源地水环境的水质评价和治理提供重要参考。This paper takes Shimen Reservoir as an example,by improving the Grasshopper Algorithm(IGOA)to increase the generalization of local values,and optimizing the parameters of the RBF neural network model,the IGOA-RBF water quality prediction model with high efficiency and high prediction accuracy is constructed.The results show that the error of IGOA-RBF water quality prediction model is small,which can provide important reference for water quality evaluation and treatment of water environment in water source.

关 键 词：IGOA-RBF 连续流动分析法 总磷 总氮 水质预测模型 石门水库 

分 类 号：X832[环境科学与工程—环境工程]