Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction
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...
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| Veröffentlicht in: | Water, air, and soil pollution air, and soil pollution, 2014-08, Vol.225 (8), p.1-9, Article 2058 |
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| creator | Albergaria, José Tomás Martins, F. G Alvim-Ferraz, M. C. M Delerue-Matos, C |
| description | 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. |
| doi_str_mv | 10.1007/s11270-014-2058-y |
| format | Article |
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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.</description><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Benzene</subject><subject>Bioremediation</subject><subject>Climate Change/Climate Change Impacts</subject><subject>Contamination</subject><subject>Decision theory</subject><subject>Earth and Environmental Science</subject><subject>Efficiency</subject><subject>Environment</subject><subject>Environmental health</subject><subject>Environmental monitoring</subject><subject>Ethyl benzene</subject><subject>Ethylbenzene</subject><subject>Experiments</subject><subject>Extraction</subject><subject>Heavy metals</subject><subject>Hydrocarbons</subject><subject>Hydrogeology</subject><subject>linear models</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Perchloroethylene</subject><subject>polluted soils</subject><subject>prediction</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Remediation</subject><subject>Soil (material)</subject><subject>Soil contaminants</subject><subject>Soil contamination</subject><subject>Soil pollution</subject><subject>Soil remediation</subject><subject>Soil Science & Conservation</subject><subject>soil vapor extraction</subject><subject>Solvents</subject><subject>Studies</subject><subject>Tetrachloroethylene</subject><subject>Toluene</subject><subject>Trichloroethylene</subject><subject>Vapors</subject><subject>viability</subject><subject>VOCs</subject><subject>Volatile organic compounds</subject><subject>Water Quality/Water Pollution</subject><subject>Xylene</subject><issn>0049-6979</issn><issn>1573-2932</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqFkU9v1DAQxSMEEkvbD8AJS1y4pHgcO7aPq2oLSAtF_cPVcpLxyiUbb-1EsN8eZ8MBcYCZw8jW743e6BXFa6CXQKl8nwCYpCUFXjIqVHl8VqxAyKpkumLPixWlXJe1lvpl8SqlR5pLK7kqnj5P_egPPZKtH9BGcou7iCn5MBA7dGQdR-98621PvuAUT2P8EeL3RMZAvkbsfDuSe7_HE75xM4xDeyTBkbvge_LNHkIkm59jtO2Y154XL5ztE178nmfFw_Xm_upjub358OlqvS2tqMVYNgqEBWqb_NJadxoEgBQIitVAXVMzLWUHqlFVW7sOa1AVh6bhDaMcpK3OinfL3kMMTxOm0ex9arHv7YBhSgZqAZzzmsH_UVFTYAqqGX37F_oYpjjkQzIlWDbJuM7U5ULtbI_GDy7M1-fucO_bMKDz-X9dKZ5ToCcHsAjaGFKK6Mwh-r2NRwPUzAGbJWCTAzZzwOaYNWzRpMwOO4x_WPmH6M0icjYYu4s-mYc7lgFKodKKy-oX2WSv4w</recordid><startdate>20140801</startdate><enddate>20140801</enddate><creator>Albergaria, José Tomás</creator><creator>Martins, F. 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| subjects | Analysis Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Benzene Bioremediation Climate Change/Climate Change Impacts Contamination Decision theory Earth and Environmental Science Efficiency Environment Environmental health Environmental monitoring Ethyl benzene Ethylbenzene Experiments Extraction Heavy metals Hydrocarbons Hydrogeology linear models Mathematical models Neural networks Perchloroethylene polluted soils prediction Regression Regression analysis Remediation Soil (material) Soil contaminants Soil contamination Soil pollution Soil remediation Soil Science & Conservation soil vapor extraction Solvents Studies Tetrachloroethylene Toluene Trichloroethylene Vapors viability VOCs Volatile organic compounds Water Quality/Water Pollution Xylene |
| title | Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction |
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