OTIMIZAÇÃO DA BIOPROSPECÇÃO DE ATIVOS BIOLÓGICOS POR INTELIGÊNCIA ARTIFICIAL
DOI:
https://doi.org/10.56238/revgeov17n4-029Palavras-chave:
Bioprospecção, Inteligência Artificial, Random Forest, Otimização de ProspecçãoResumo
O presente estudo examina a identificação de ativos biológicos por meio da união estratégica entre dados ambientais georreferenciados e modelos preditivos, com base em inteligência artificial (IA). Seu objetivo principal é a otimização dos processos e a diminuição das despesas operacionais na bioprospecção tradicional. Para tal, parte-se do pressuposto de que a biodiversidade terrestre é um vetor de ativo de valor incalculável, evidenciando a urgência em se criar e operacionalizar metodologias úteis para lidar com essa complexidade, transpondo as limitações das metodologias tradicionais. Desse modo, aplicou-se o algoritmo Random Forest junto a rasters climáticos, shapefiles e dados do Sistema de Informação sobre a Biodiversidade Brasileira (SiBBr), visando mapear áreas de elevado potencial bioativo. A metodologia foi testada por meio de protótipo em R Shiny, que possibilitou simular cenários pragmáticos e mapear riscos operativos. Mesmo os resultados preliminares apontando para a necessidade de refino paramétrico, os achados ratificam o potencial das ferramentas de IA em servir como agentes catalisadores à bioeconomia sustentável na Amazônia.
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