期刊
JOURNAL OF APPLIED RESEARCH AND TECHNOLOGY
卷 12, 期 3, 页码 493-499出版社
UNIV NACIONAL AUTONOMA MEXICO
DOI: 10.1016/S1665-6423(14)71629-3
关键词
back propagation neural network; genetic algorithm; principal component analysis; water quality prediction
类别
-
资金
- National Natural Science Foundation of China [21001053]
- National High Technology Research and Development Program [2009AA02C210]
- Fundamental Research Funds for the Central Universities [JUSRP11126]
To effectively control and treat river water pollution, it is very critical to establish a water quality prediction system. Combined Principal Component Analysis (PCA), Genetic Algorithm (GA) and Back Propagation Neural Network (BPNN), a hybrid intelligent algorithm is designed to predict river water quality. Firstly, PCA is used to reduce data dimensionality. 23 water quality index factors can be compressed into 15 aggregative indices. PCA improved effectively the training speed of follow-up algorithms. Then, GA optimizes the parameters of BPNN. The average prediction rates of non-polluted and polluted water quality are 88.9% and 93.1% respectively, the global prediction rate is approximately 91%. The water quality prediction system based on the combination of Neural Networks and Genetic Algorithms can accurately predict water quality and provide useful support for real-time early warning systems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据