OPTIMIZACIÓN DE LA BIOPROSPECCIÓN DE ACTIVOS BIOLÓGICOS MEDIANTE INTELIGENCIA ARTIFICIAL
DOI:
https://doi.org/10.56238/revgeov17n4-029Palabras clave:
Bioprospección, Inteligencia Artificial, Random Forest, Optimización de ProspecciónResumen
El presente estudio examina la identificación de activos biológicos mediante la unión estratégica entre los datos ambientales georreferenciados y los modelos predictivos, basados en inteligencia artificial (IA). Su principal objetivo es la optimización de procesos y la reducción de los gastos operativos en la bioprospección tradicional. Para ello, se basa en la suposición de que la biodiversidad terrestre es un vector activo de valor incalculable, lo que evidencia la urgencia de crear y operacionalizar metodologías útiles para afrontar esta complejidad, superando las limitaciones de las metodologías tradicionales. Así, el algoritmo Random Forest se aplicó a rastres climáticos, shapefiles y datos de Sistema Brasileño de Información sobre la Biodiversidad (SiBBr), con el objetivo de mapear áreas de alto potencial bioactivo. La metodología se probó mediante un prototipo en R Shiny, que permitió simular escenarios pragmáticos y cartografiar riesgos operativos. Aunque los resultados preliminares apuntan a la necesidad de refinamiento paramétrico, los hallazgos ratifican el potencial de las herramientas de IA para actuar como agentes catalíticos para la bioeconomía sostenible en el Amazonas.
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