Previsão de atividade solar a partir da configuração dos campos magnéticos fotosféricos

• Ano de defesa: 2007
• Autor(a) principal: Raffaelli, Tatiana Ferreira
• Orientador(a): Silva, Adriana Valio Roque da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Presbiteriana Mackenzie
• Palavras-chave em Português: Regiões ativas. Explosões solares. Redes Bayesianas. Fusão de informação. Sistemas de previsão.
• Palavras-chave em Inglês: Active Regions. Solar flares. Bayesian networks. Fusion of information. Forecast systems.
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://dspace.mackenzie.br/handle/10899/24392

The existence of a highly reliable prediction system to detect the occurrence of large solar flares (class X) is still an unsolved problem. Despite many studies performed so far, no such a system has been found yet. In this work, we have developed a method using Bayesian Network - an Artificial Intelligence technique for the detection of giant solar flares. The Bayesian Networks software learned the relation among the variables that describe the sunspots within an active region and built a network with the relationships among them based on conditional probabilities. The studies were divided into two stages one to detect whether the sunspot would produce a big flare or not and another phase where some networks were built to discover the day the flare would occur. The first phase results were very satisfactory reaching a reliability of 77%. The second phase was more complex and the results were about 77% (with day constraints) and 54% (a wider range of days).