The Use of Combined Neural Networks and Genetic Algorithms for Prediction of River Water Quality
To effectively control and treat river water pollution, it is very critical to establish a water quality predictionsystem. Combined Principal Component Analysis (PCA), Genetic Algorithm (GA) and Back Propagation NeuralNetwork (BPNN), a hybrid intelligent algorithm is designed to predict river water...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2014 |
| País: | México |
| Recursos: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositório: | Journal of Applied Research and Technology |
| Idioma: | inglês |
| OAI Identifier: | oai:ojs2.localhost:article/210 |
| Acesso em linha: | https://jart.icat.unam.mx/index.php/jart/article/view/210 |
| Access Level: | Acceso aberto |
| Palavra-chave: | back propagation neural network genetic algorithm principal component analysis water quality prediction |
| Resumo: | To effectively control and treat river water pollution, it is very critical to establish a water quality predictionsystem. Combined Principal Component Analysis (PCA), Genetic Algorithm (GA) and Back Propagation NeuralNetwork (BPNN), a hybrid intelligent algorithm is designed to predict river water quality. Firstly, PCA is used toreduce data dimensionality. 23 water quality index factors can be compressed into 15 aggregative indices. PCAimproved 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, theglobal prediction rate is approximately 91%. The water quality prediction system based on the combination ofNeural Networks and Genetic Algorithms can accurately predict water quality and provide useful support for realtimeearly warning systems. |
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