Cork Oak Regeneration Prediction Through Multilayer Perceptron Architectures

In Mediterranean ecosystems, a thorough understanding of seedling regeneration dynamics as well as a good predictive ability of the process is essential for sustainable forest management. Leveraging the predictive capacity of the multilayer perceptron (MLP) as recognized as artificial intelligence methodology, the authors analyzed a real case study with a dataset encompassing environmental, ecological, and forestry variables. The study focused on the cork oak (Quercus suber, L.) seedling regeneration dynamic, which is a critical process for maintaining ecosystem resilience. A set of 10 MLP with a block from 5 to 50 neurons with hyperbolic tangent (TanH), linear (LIN), and Gaussian (GAUS) activation function were tested and their performance for predictive purposes was compared with traditional quantitative approaches. The MLP configured with 40–50 neurons per activation function (TanH, LIN, GAUS) demonstrated outstanding predictive performance, achieving an area under the curve (AUC) of the receiver operating characteristic and precision-recall scores above 0.80. These models made few prediction errors, effectively explaining the majority of the data variance, as indicated by a high generalized R2 and a low mislearning ratio. This approach outperformed traditional statistical models in predicting seedling regeneration. Tree density, stand density index, and acorn number played an important role, influencing the cork oak seedling prediction. In conclusion, the results of this research determined the importance of an AI classification modeling technique in the prediction of cork oak regeneration, providing practical references for future forest management strategy decisions.