Use of artificial neural networks with the physiological principles do predict growth model

Silva G.C.C.; Neves J.C.L.; Marcatti G.E.; Soares C.P.B.; Calegario N.; Araújo Júnior C.A.; Gonzáles D.G.E.; Gleriani J.M.; Binoti D.H.B.; Paiva H.N.D.; Leite H.G. · Engineering Applications of Artificial Intelligence (2024) · DOI: 10.1016/j.engappai.2024.108914

Resumen

Process-based models have become essential tools for forest planning and management due to their ability to assess the impacts of climate and operational changes on productivity. Physiological Principles Predicting Growth (3-PG) is a process-based/hybrid model widely used, associated with allometric models to estimate dendrometric variables. However, the accuracy of the model outputs still needs to be improved, especially those of interest to forest management. In this study, we tested the efficiency of artificial neural networks (ANNs) to estimate the average diameter at 1.30 m height (dbh), total height, stand volume, and root, stem, and foliage biomass. We also evaluated the prediction of two parameters that estimate the proportion of biomass allocated aboveground (ap and np). A database from published 3-PG parameterizations was generated and used to train the ANNs. Two networks were validated against observed data from two municipalities in the MG state of Brazil. Potential productivity maps at six and seven years were generated for Brazil, to evaluate the performance of the 3-PG + ANN model. All ANNs presented satisfactory statistical results in predicting the ap and np parameters, foliage biomass, dbh, total height and stand volume. The use of artificial neural networks integrated with the 3-PG model or another process-based model is a promising alternative to improve the accuracy of estimates, especially those of interest for forest management. Artificial neural networks offer a simple, flexible way to insert important variables into a process-based model. The parameters and 3-PG outputs can be estimated efficiently using artificial neural networks.

Instituciones: Universidad Nacional Autónoma de Chota