The Use of Combined Neural Networks and Genetic Algorithms for Prediction of River Water Quality
Título del documento: The Use of Combined Neural Networks and Genetic Algorithms for Prediction of River Water Quality
Revista: Journal of applied research and technology
Base de datos: PERIÓDICA
Número de sistema: 000384255
ISSN: 1665-6423
Autores: 1
2
3
4
Instituciones: 1Jiang Nan University, Department of Computer Science and Technology, Wuxi, Jiangsu. China
2Jiang Nan University, School of Biotechnology, Wuxi, Jiangsu. China
3Jiang Nan University, School of Chemical and Material Engineering, Wuxi, Jiangsu. China
4Environmental Monitoring Station of Binhu District, Wuxi, Jiangsu. China
Año:
Periodo: Jun
Volumen: 12
Número: 3
Paginación: 493-499
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés 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 realtime early warning systems
Disciplinas: Ingeniería,
Geociencias
Palabras clave: Ingeniería ambiental,
Hidrología,
Ríos,
Calidad del agua,
Análisis de componentes principales,
Algoritmos genéticos
Keyword: Engineering,
Earth sciences,
Environmental engineering,
Hydrology,
Rivers,
Water quality,
Principal component analysis,
Genetic algorithms
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