Modelo de predição do crescimento e produção para plantios de tachi-branco baseado em autômato celular e aprendizado de máquina

• Autor(a) principal: Freitas, Pablo Henrique de
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/39974; http://doi.org/10.14393/ufu.di.2023.562
• topic: Autômato celular; Aprendizado de máquina; Florestas plantadas; Predição da produção; Cellular Automata; Machine Learning; Production prediction; Managed Forest; Managed Forest; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO::LINGUAGEM FORMAIS E AUTOMATOS; Computação; Automato celular; Aprendizado do computador; Florestas - Inovações tecnológicas; ODS::ODS 15. Vida terrestre - Proteger, recuperar e promover o uso sustentável dos ecossistemas terrestres, gerir de forma sustentável as florestas, combater a desertificação, deter e reverter a degradação da Terra e deter a perda da biodiversidade.

The dynamics of forest plantations have been widely studied with computational simulation applications. Cellular automata (CA) is a technique capable of modelling future states based on a set of transition rules. However, this construction is not simple, often requiring technical knowledge of the process through years of scientific research. Machine learning techniques can be applied in this context, facilitating the construction of these simulators. According to this scenario, this work presents a simulation model based on probabilistic cellular automata capable of estimating the evolution of wood production throughout the management period. Unlike other works in the literature, the construction of the AC transition rule is based exclusively on historical data from a Tachi-branco plantation, a managed forest species. Linear and logistic regression models are applied to learn and represent the local transition rules of the automaton and simulate its evolution. The proposed CA-based approach was able to predict the future behavior of plantations in the monitored areas with errors around 4%, confirming the potential of using machine learning in discovering transition rules for precise models based on cellular automata.