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

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Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorSão Paulo Environmental Protection Agency-
Autor(es): dc.contributorSorocaba Engineering College-
Autor(es): dc.contributorSantos Paulista University-
Autor(es): dc.creatorBressane, Adriano [UNESP]-
Autor(es): dc.creatorda Silva, Pedro Modanez [UNESP]-
Autor(es): dc.creatorFiore, Fabiana Alves [UNESP]-
Autor(es): dc.creatorCarra, Thales Andrés-
Autor(es): dc.creatorEwbank, Henrique-
Autor(es): dc.creatorDe-Carli, Bruno Paes-
Autor(es): dc.creatorda Mota, Maurício Tavares [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:26:39Z-
Data de disponibilização: dc.date.available2022-02-22T00:26:39Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.eiar.2020.106446-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/199174-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/199174-
Descrição: dc.descriptionScreening 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.-
Descrição: dc.descriptionUnesp São Paulo State University, Eng. Francisco José Longo Avenue-
Descrição: dc.descriptionCetesb São Paulo Environmental Protection Agency, Prof. Frederico Hermann Jr. Avenue-
Descrição: dc.descriptionUniFacens Sorocaba Engineering College, Rodovia Senador José Ermírio de Moraes-
Descrição: dc.descriptionUnip Institute of Health Santos Paulista University, Francisco Manoel Avenue-
Descrição: dc.descriptionUnesp São Paulo State University, March 03 Avenue-
Descrição: dc.descriptionUnesp São Paulo State University, Eng. Francisco José Longo Avenue-
Descrição: dc.descriptionUnesp São Paulo State University, March 03 Avenue-
Idioma: dc.languageen-
Relação: dc.relationEnvironmental Impact Assessment Review-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectComplexity-
Palavras-chave: dc.subjectDecision-making-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectProject appraisal-
Título: dc.titleFuzzy-based computational intelligence to support screening decision in environmental impact assessment: A complementary tool for a case-by-case project appraisal-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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