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

Güner Ş. T., Diamantopoulou M. J., Poudel K. P., Çömez A., ÖZÇELİK R.

Computers and Electronics in Agriculture, cilt.192, 2022 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 192
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.compag.2021.106596
  • Dergi Adı: Computers and Electronics in Agriculture
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, BIOSIS, CAB Abstracts, Communication Abstracts, Computer & Applied Sciences, Environment Index, Food Science & Technology Abstracts, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Dirichlet regression, Levenberg-Marquardt artificial neural network, Nonlinear seemingly unrelated regression, Tree biomass
  • Isparta Uygulamalı Bilimler Üniversitesi Adresli: Evet

Özet

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.