Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Autor: A. Yousafzai, W. Manzoor, G. Raza, T. Mahmood, F. Rehman, R. Hadi, S. Shah, M. Amin, A. Akhtar, S. Bashir, U. Habiba, M. Hussain

Publikováno v: Brazilian Journal of Biology, Vol 84 (2021)

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regr

Externí odkaz: https://doaj.org/article/bd6d31f6a2054bd9af5026acda0bcc66

Previsão de produção florestal em diferentes cenários de mudanças climáticas usando modelos inteligentes de dados no Paquistão

Autor: Wajiha Manzoor, A. Yousafzai, Majid Hussain, M. Amin, S. Bashir, G. Raza, Fariha Rehman, R. Hadi, S. Shah, Ume Habiba, Tahir Mahmood, Andleeb Akhtar

Publikováno v: Brazilian Journal of Biology, Vol 84 (2021)
Brazilian Journal of Biology, Volume: 84, Article number: e253106, Published: 29 OCT 2021
Brazilian Journal of Biology v.84 2024
Brazilian Journal of Biology
Instituto Internacional de Ecologia (IIE)
instacron:IIE

This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (K

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6e12cecf0f598b04f3a7334fc7fd9da
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842024000100109&tlng=en