Fuzzy-based computational intelligence to support screening decision in environmental impact assessment: A complementary tool for a case-by-case project appraisal

dc.contributor.authorBressane, Adriano [UNESP]
dc.contributor.authorda Silva, Pedro Modanez [UNESP]
dc.contributor.authorFiore, Fabiana Alves [UNESP]
dc.contributor.authorCarra, Thales Andrés
dc.contributor.authorEwbank, Henrique
dc.contributor.authorDe-Carli, Bruno Paes
dc.contributor.authorda Mota, Maurício Tavares [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionSão Paulo Environmental Protection Agency
dc.contributor.institutionSorocaba Engineering College
dc.contributor.institutionSantos Paulista University
dc.date.accessioned2020-12-12T01:32:47Z
dc.date.available2020-12-12T01:32:47Z
dc.date.issued2020-11-01
dc.description.abstractScreening is a key stage in environmental impact assessment (EIA), but the most common approach based on policy delineation are inherently arbitrary. On the other hand, a case-by-case approach can be complex, slow, and costly. This paper introduces a computational intelligence based on hybrid fuzzy inference system (h-FIS), combining data-driven and expert knowledge, in order to assess its capability of supporting a case-by-case screening in project appraisal. For empirical research, a dataset with appraisal variables of projects highway was made available by a Brazilian environmental protection agency (EPA). Firstly, using this dataset, multivariate analyses were performed to find criteria (xi) capable of indicating statistically significant differences among projects, previously screened by EPA experts into three types (simplified, preliminary, and comprehensive) of environmental impact study (EIS). Then, h-FIS was built through machine learning, using the FRBCS·W algorithm, with xi as input predictors and the type of EIS as the output target. The performances of alternative approaches were compared using cross-validation accuracy tests and the kappa index, with a significance level of 0.05. As a result, the h-FIS achieved accuracy of 92.6% and a kappa index of 0.88, which represented almost perfect agreement between the screening decision provided by the h-FIS and the one performed by the EPA experts. In conclusion, the fuzzy-based computational intelligence was capable of dealing with the complexity involved in screening decision. Therefore h-FIS be considered a promising complementary tool for a case-by-case project appraisal in EIA. For further advances, future research should assess other algorithms, such as genetic fuzzy systems, in order to strengthen the proposed system and make it generally applicable in other projects subject to EIA.en
dc.description.affiliationUnesp São Paulo State University, Eng. Francisco José Longo Avenue
dc.description.affiliationCetesb São Paulo Environmental Protection Agency, Prof. Frederico Hermann Jr. Avenue
dc.description.affiliationUniFacens Sorocaba Engineering College, Rodovia Senador José Ermírio de Moraes
dc.description.affiliationUnip Institute of Health Santos Paulista University, Francisco Manoel Avenue
dc.description.affiliationUnesp São Paulo State University, March 03 Avenue
dc.description.affiliationUnespUnesp São Paulo State University, Eng. Francisco José Longo Avenue
dc.description.affiliationUnespUnesp São Paulo State University, March 03 Avenue
dc.identifierhttp://dx.doi.org/10.1016/j.eiar.2020.106446
dc.identifier.citationEnvironmental Impact Assessment Review, v. 85.
dc.identifier.doi10.1016/j.eiar.2020.106446
dc.identifier.issn0195-9255
dc.identifier.lattes9905539715619645
dc.identifier.orcid0000-0002-2430-8240
dc.identifier.scopus2-s2.0-85088656445
dc.identifier.urihttp://hdl.handle.net/11449/199174
dc.language.isoeng
dc.relation.ispartofEnvironmental Impact Assessment Review
dc.sourceScopus
dc.subjectComplexity
dc.subjectDecision-making
dc.subjectMachine learning
dc.subjectProject appraisal
dc.titleFuzzy-based computational intelligence to support screening decision in environmental impact assessment: A complementary tool for a case-by-case project appraisalen
dc.typeArtigo
unesp.author.lattes9905539715619645(3)
unesp.author.orcid0000-0002-2430-8240(3)

Arquivos

Coleções