Abstract:
:As soil is a natural resource not always renewable, the risk characterization of contaminated soils is an issue of great interest. Artificial Intelligence (AI), based on Decision Support Systems (DSSs), has been developed for a wide range of applications in contaminated soil management. Decision trees have already shown to be easy to interpret and able to treat large scale applications. Fuzzy logic gives an improvement in the perturbations and the variance of the training data, due to the elasticity of fuzzy set formalism. In this study, we have developed a classificatory tool applied to characterize contaminated soil in function of human and environmental risks. Knowledge engineering for constructing the Soil Risk Characterization Decision Support System (SRC-DSS) involves three stages: knowledge acquisition, conceptual design and system implementation. A total of 26 parameters were divided into three groups to facilitate the configuration of the expert system: source attributes, transfer vector attributes, and local properties. Sixteen case studies were evaluated with the SRC-DSS. In comparison with other techniques, the results of the current study have shown that SRC-DDS is an excellent tool to classify and characterize soils according to the associated risk.
journal_name
Environ Intjournal_title
Environment internationalauthors
López EM,García M,Schuhmacher M,Domingo JLdoi
10.1016/j.envint.2008.02.005subject
Has Abstractpub_date
2008-10-01 00:00:00pages
950-8issue
7eissn
0160-4120issn
1873-6750pii
S0160-4120(08)00026-3journal_volume
34pub_type
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pub_type: 杂志文章
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journal_title:Environment international
pub_type: 杂志文章
doi:10.1016/j.envint.2007.07.002
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pub_type: 杂志文章,评审
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journal_title:Environment international
pub_type: 杂志文章
doi:10.1016/S0160-4120(02)00113-7
更新日期:2003-03-01 00:00:00
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journal_title:Environment international
pub_type: 杂志文章
doi:10.1016/j.envint.2013.11.013
更新日期:2014-02-01 00:00:00
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journal_title:Environment international
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pub_type: 杂志文章
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pub_type: 杂志文章
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journal_title:Environment international
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pub_type: 杂志文章
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