Goycochea Casas, GianmarcoBaselly Villanueva, Juan RodrigoCoimbra Limeira, Mathaus MessiasEleto Torres, Carlos Moreira MiquelinoGarcia Leite, Hélio2023-10-022023-10-022023-09-21Casas, G.; Baselly, J.; Limeira, M; Torres, C.; & Leite, H. (2023). Classifying the risk of forest loss in the Peruvian Amazon Rainforest: An alternative approach for sustainable forest management using artificial intelligence. Trees, Forests and People, 100440. doi: 10.1016/j.tfp.2023.1004402666-7193https://hdl.handle.net/20.500.12955/2293Peruvian Amazonian rainforests are constantly threatened by forest loss. Understanding changes in forest cover and assessing the level of risk is a permanent concern for numerous scientists and forest authorities. There are many conservation programs for Peruvian forests that involve collaborative efforts and employ diverse methodologies for forest monitoring. In this study, we propose an alternative approach to decision-making for forest preservation, aiming to classify the risk of forest loss in districts within the Peruvian Amazon rainforest. This classification enables sustainable forest management. To accomplish this, we utilized unsupervised learning artificial intelligence through Kohonen's neural network. The network was trained using a historical database spanning from 2001 to 2021, which includes variables such as forest cover and loss, climate, topography, hydrographic networks, and timber forest concessions. Through this approach, the network successfully established five clusters. Following preliminary analysis, we designated these clusters as: low, medium, high, very high, and extremely high risk of forest loss. Kohonen networks demonstrated their effectiveness in clustering forest loss and forest cover. The results indicate a shifting trend among the classes over time, with an increase in the categories exhibiting high and very high risk of forest cover loss. This study provides valuable information for decision-making in the prevention and conservation of Peruvian forests. We strongly recommend maintaining vigilance, particularly in districts classified as a very high or extremely high risk of losing forest cover.application/pdfenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Kohonen neural networkForest conservationForest preventionForest preventioninfo:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#4.01.02https://doi.org/10.1016/j.tfp.2023.100440Forest conservationConservación de montesForest protectionProtección forestalArtificial intelligenceInteligencia artificial