Sistemas inteligentes aplicados à análise de riscos ambientes

• Ano de defesa: 2012
• Autor(a) principal: Albuquerque Filho, Francisco Sales de
• Orientador(a): Fernandes, Sérgio Murilo Maciel
• Banca de defesa: Lima, Emerson Alexandre de Oliveira , Ferreira, Tiago Alessandro Espinola
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Católica de Pernambuco
• Programa de Pós-Graduação: Mestrado em Desenvolvimento de Processos Ambientais
• Departamento: Desenvolvimento de Processos Ambientais
• País: BR
• Palavras-chave em Português: avaliação de riscos ambientais; poluição - aspectos ambientais; redes neurais (computação); inteligência artificial; lógica difusa; dissertações
• Palavras-chave em Inglês: environmental risk assessment; pollution - environmental aspects; neural networks (computer science); artificial intelligence; nonlinear logic; dissertations
• Área do conhecimento CNPq: CNPQ::CIENCIAS BIOLOGICAS::BIOLOGIA GERAL
• Link de acesso: http://tede2.unicap.br:8080/handle/tede/620

In order to forecast and classify environmental risks, artificial intelligence (AI) techniques were applied to the air quality problem. Predetermined gaseous pollutant concentration data were acquired with the intent of predicting the risks. Such concentrations are denominated air quality indicators, and are regulated all around the world, including by brazilian law. The data concerning these indicators were used in a model that consists of two AI techniques: artificial neural networks and particle swarm optimization. The air quality indicators concentration prediction resulted in one day ahead values. The risk modeling utilizes the predictions as inputs values, correlating them in order to obtain the resulting air quality and, the risk that such quality has upon the human health. The risk model is based on a third AI technique, called fuzzy logic. The present work obtained two main results. The first was the accurate forecasts made by the prediction model. The second was the achievement of a coherent classification of the risks.