Comparing machine learning algorithms for simultaneous prediction of tree diameter distribution percentiles
Yazarlar (12)
Albert Ciceu
Bundesforschungszentrum Für Wald, Avusturya
Bundesforschungszentrum Für Wald, Avusturya
Dr. Öğr. Üyesi Hasan AKSOY Sinop Üniversitesi, Türkiye
Ovidiu Badea National Institute For Research And Development İn Forestry,"Marin Drăcea", Romanya
Bronson P. Bullock University Of Georgia, Amerika Birleşik Devletleri
Jacinta Ukamaka Ezenwenyi
Nnamdi Azikiwe University, Nijerya
Nnamdi Azikiwe University, Nijerya
Jose Javier Gorgoso-Varela Universidade De Santiago De Compostela, İspanya
Ştefan Leca National Institute For Research And Development İn Forestry,"Marin Drăcea", Romanya
Thomas Ledermann
Bundesforschungszentrum Für Wald, Avusturya
Bundesforschungszentrum Für Wald, Avusturya
Harri Mäkinen Natural Resources Institute Finland (Luke), Finlandiya
Friday N. Ogana
Virginia Polytechnic Institute And State University, Amerika Birleşik Devletleri
Virginia Polytechnic Institute And State University, Amerika Birleşik Devletleri
Sheng-I Yang
University Of Georgia, Amerika Birleşik Devletleri
University Of Georgia, Amerika Birleşik Devletleri
Lauri Mehtätalo Natural Resources Institute Finland (Luke), Finlandiya
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Ecological Informatics (Q1) | ||
| Dergi ISSN | 1574-9541 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 10-2025 |
| Cilt / Sayı / Sayfa | 92 / 1 / 103500–0 | DOI | 10.1016/j.ecoinf.2025.103500 |
| Makale Linki | https://doi.org/10.1016/j.ecoinf.2025.103500 | ||
| UAK Araştırma Alanları |
Orman Hasılatı ve Amenajmanı
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| Özet |
| Accurate predictions of tree diameter distributions are important for assessing forest structure, quantifying biodiversity, and estimating carbon sequestration. Percentile-based approaches are among the most effective methods for reconstructing diameter distributions from stand-level variables. In this study, we compared three modelling approaches, generalised least squares (GLS), Multi-Output Random Forest (MORF), and a multi-output deep learning-based model (MODL), across nine datasets representing different forest types and management regimes, aiming to predict simultaneously six diameter distribution percentiles. Our results show that MODL consistently outperformed both GLS and MORF in predictive accuracy across all nine training subsets and five out of nine test subsets, demonstrating strong generalisation across diverse forest types. MODL was particularly effective in achieving high accuracy while … |
| Anahtar Kelimeler |
| Machine learning | Model comparison | Multi-output regression | Percentile prediction | Prediction | Tree diameter distribution |
Science Direct
Comparing machine learning algorithms for simultaneous prediction of tree diameter distribution percentiles
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 3 |
| Scopus | 2 |
| Google Scholar | 3 |