Optimum-Path Forest Ensembles to Estimate the Internal Decay in Urban Trees - Candido, Giovani [UNESP]; Morelli, Luis Henrique [UNESP]; Jodas, Danilo Samuel [UNESP]; Velasco, Giuliana Del Nero; de Lima, Reinaldo Araújo; da Costa, Kelton Augusto Pontara [UNESP]; Papa, João Paulo [UNESP] (INSTICC, 2025)
Research on urban tree management has recently grown to include various studies using machine learning to address the tree’s risk of falling. One significant challenge is to assess the extent of internal decay, a crucial factor contributing to tree breakage. This paper uses machine and ensemble learning algorithms to determine internal trunk decay levels. Notably, it introduces a novel variation of the Optimum-Path Forest (OPF) ensemble pruning method, OPFsemble, which incorporates a “count class” strategy and performs weighted majority voting for ensemble predictions. To optimize the models’ hyperparameters, we employ a slime mold-inspired metaheuristic, and the optimized models are then applied to the classification task. The optimized hyperparameters are used to randomly select distinct configurations for each model across ensemble techniques such as voting, stacking, and OPFsemble. Our OPFsemble variant is compared to the original one, which serves as a baseline. Moreover, the estimated levels of internal decay are used to predict the tree’s risk of falling and evaluate the proposed approach’s reliability. Experimental results demonstrate the effectiveness of the proposed method in determining internal trunk decay. Furthermore, the findings reveal the potential of the proposed ensemble pruning in reducing ensemble models while attaining competitive performance.
Keywords: Ensemble Learning; Internal Trunk Decay; Machine Learning; Metaheuristics; Urban Tree Risk Management