摘要
Abstract
The traditional BP neural network model has the disadvantages of low training speed and difficulty in parameter selection, and it can easily fall into local extremum issues .In order to solve these problems, a water quality prediction model, the extreme learning machine (ELM), is proposed.The model was applied to a reservoir in Yunnan Province.NH3-N, NO-2-N, NO 3-N, CODMn , and water transparency were selected as the network-inputs, and TP and TN were selected as the outputs to build the TP and TN prediction model based on ELM .The predicted results of ELM were compared with those of the traditional BP , GA-BP, and RBF neural network models. The results show that the ELM model ’s prediction accuracy was higher than those of the traditional BP and RBF neural network models, and even slightly higher than that of the GA-BP model.In addition, ELM model parameter selection is simple, has a capability for fast training, and is not likely to fall into local optimum values; it has a large computational advantage .关键词
极限学习机/人工神经网络模型/GA-BP/BP/RBF/水质预测/湖库Key words
extreme learning machine/artificial neural network model/GA-BP/BP/RBF/water quality prediction/lakes and reservoirs分类
资源环境
