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Métodos causales de aprendizaje automático para comprender el cambio de uso y cobertura del suelo
F Eigenbrod1, Peter Alexander2, Nicolas Apfel3
1School of Geography and Environmental Science, University of Southampton, Southampton, SSO17 1BJ UK.
Resumen
Los métodos causales de aprendizaje automático (ML) ofrecen un enfoque prometedor para comprender los impulsores del cambio de uso y cobertura del suelo (LULCC). La integración de estos métodos avanzados con la experiencia en el dominio es clave para el diseño eficaz de políticas de LULCC.
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