Employing artificial neural network for effective biomass prediction: An alternative approach

• Published in: Computers and electronics in agriculture 2022-01, Vol.192, p.106596, Article 106596
• Main Authors: Güner, Şükrü Teoman, Diamantopoulou, Maria J., Poudel, Krishna P., Çömez, Aydın, Özçelik, Ramazan
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biomass; Dirichlet problem; Dirichlet regression; Forest management; Levenberg-Marquardt artificial neural network; Modelling; Neural networks; Nonlinear seemingly unrelated regression; Optimization; Strategic management; Tree biomass; Wood products
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2021.106596

[Display omitted] •Accurate estimation of total tree biomass and its components is critical.•The NSUR, Dirichlet regressions and LMANN approaches compared.•Artificial Neural Networks have the potential to improve estimates of biomass.•The LMANN approach is considered as a promising alternative for biomass prediction. Wood products and energy production originating from harnessing the tree biomass require optimizing the forest management process so as to ensure the sustainability of the forest ecosystems. This optimization can also act as a preventive factor towards limiting the consequences of climate change given it is a contributing factor for maintaining healthy ecosystems. To that end, the need to develop methodologies that enable accurate prediction of biomass is more than evident. Nonlinear seemingly unrelated regressions, Dirichlet regressions, and the Levenberg-Marquardt artificial neural network (LMANN) modeling techniques have been applied for whole tree (above and below ground) biomass prediction as well as its components. We conducted a comparative analysis of these approaches using destructively sampled black pine (Pinus nigra Arnold.) trees. Results showed that the LMANN models are flexible and fit tree biomass data with the highest accuracy. Inherent deviations of the biomass data from regression assumptions further support the use of LMANN models as a reliable and promising alternative to the other modeling approaches.