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Regression models

Autor: Carstensen, Bendix, author

Publikováno v: Epidemiology with R, 2020, ill.

Externí odkaz: https://doi.org/10.1093/oso/9780198841326.003.0005

Intelligent Models Performance Improvement Based on Wavelet Algorithm and Logarithmic Transformations in Suspended Sediment Estimation

Autor: R. Hajiabadi, S. Farzin, Y. Hassanzadeh

Publikováno v: مجله آب و خاک, Vol 30, Iss 1, Pp 112-124 (2016)

Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or remove

Externí odkaz: https://doaj.org/article/4d5326bad37f4e0caa5e20b6715b2ed6

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Allometric Biomass Models for European Beech and Silver Fir: Testing Approaches to Minimize the Demand for Site-Specific Biomass Observations

Autor: Jorge Curiel Yuste, Any Mary Petrițan, Dimitris Zianis, Ion Cătălin Petrițan, Gheorghe Ștefan, Cosmin Braga, Ioan Dutcă

Publikováno v: Forests, Vol 11, Iss 1136, p 1136 (2020)
Forests
Volume 11
Issue 11
Addi: Archivo Digital para la Docencia y la Investigación
Universidad del País Vasco
Addi. Archivo Digital para la Docencia y la Investigación
instname

In this paper, site-specific allometric biomass models were developed for European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) to estimate the aboveground biomass in Șinca virgin forest, Romania. Several approaches to minimize the d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c39d2806557474018782c7800af6fbec
https://www.mdpi.com/1999-4907/11/11/1136