Multiple linear regression and artificial neural networks to predict time and efficiency of soil vapor extraction

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

José Tomás Albergaria (Autor) Outra; F. G. Martins (Autor) FEUP; M. C. M. Alvim-Ferraz (Autor) FEUP; C. Delerue Matos (Autor) Outra

Revista

Título: Water, Air, and Soil Pollution

Vol. 225 Nº 8

Páginas: 1-9

ISSN: 0049-6979

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 5 Citações

Projetos Associados

UI0511 - PROJECTO ESTRATÉGICO - UI 511 - 2013-2014

Outras Informações

ID Authenticus: P-009-RVC

DOI: 10.1007/s11270-014-2058-y

Abstract (EN): 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. © 2014 Springer International Publishing.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9