Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5313
Título: Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction
Autor: Albergaria, José Tomás
Martins, F.G.
Alvim-Ferraz, Maria da Conceição M.
Delerue-Matos, Cristina
Palavras-chave: Soil vapor extraction
Artificial neural networks
Multiple linear regression
Remediation time
Process efficiency
Data: Jul-2014
Editora: Springer International Publishing
Relatório da Série N.º: Water, Air, & Soil Pollution;Vol. 225
Resumo: The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5313
DOI: 10.1007/s11270-014-2058-y
Versão do Editor: http://link.springer.com/article/10.1007%2Fs11270-014-2058-y
Aparece nas colecções:ISEP – GRAQ – Artigos

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