A modification of the Ek–Payandeh nonlinear regression model for site index curves

1988 ◽  
Vol 18 (1) ◽  
pp. 115-120 ◽  
Author(s):  
R. M. Newnham
Keyword(s):  
Regression Model ◽  
Growth Model ◽  
Nonlinear Regression ◽  
Site Index ◽  
The Other ◽  
Nonlinear Regression Model ◽  
Minimum Loss ◽  
Unconstrained Model ◽  
Pass Through ◽  
Richards Growth Model

A new nonlinear regression model for constructing site index curves is described that, unlike previous models based on the Chapman–Richards growth model, produces sets of curves that are constrained to pass through the appropriate height at the index age as well as through the origin. The model was tested on two sets of height–age data, one from published yield tables and the other from stem analyses, and was found to give a good fit in both cases. There was a minimum loss of accuracy compared with a similar, unconstrained model.

Polymorphic height and site index curves for lodgepole pine in Alberta

1989 ◽  
Vol 19 (9) ◽  
pp. 1151-1160 ◽  
Author(s):  
C. J. Cieszewski ◽  
I. E. Bella
Keyword(s):  
Regression Model ◽  
Prior Knowledge ◽  
Nonlinear Regression ◽  
Lodgepole Pine ◽  
Site Index ◽  
The Other ◽  
Nonlinear Regression Model ◽  
Permanent Sample Plots ◽  
Accuracy And Precision ◽  
Pass Through

This new, biologically based, nonlinear regression model produces polymorphic site index and height curves as a function of prediction age and a height at any age. The curves are constrained to pass through the origin and appropriate heights at any index age. The model was parametrized on 970 stem-analyzed trees and tested on tree measurements from 147 permanent sample plots. Compared with other lodgepole pine (Pinuscontorta var. latifolia Engelm.) height models in Alberta, this model had fewer parameters, yet showed better accuracy and precision than the other models. Above all, the new model provides compatible site index and height estimates, and it can predict height without prior knowledge of site index.

scholarly journals Site-Index Curves and Growth Intercepts for Young White Spruce Plantations in North Central Ontario

2006 ◽  
Vol 23 (4) ◽  
pp. 257-263 ◽  
Author(s):  
Willard H. Carmean ◽  
Gerrit Hazenberg ◽  
James S. Thrower ◽  
Richard R. LaValley
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
White Spruce ◽  
Analysis Data ◽  
Site Index ◽  
Nonlinear Regression Model ◽  
North Central ◽  
Breast Height ◽  
University Of Wisconsin ◽  
Dominant Height

Abstract Site-index (heightߝgrowth) curves, site-index prediction equations, and growth intercepts were developed from internode measurements and stem-analysis data using dominant trees in 69 plots located in white spruce plantations aged 19 to 32 years total age. Site-index curves were based on breast-height (1.3 m) age because our analyses show that height growth below breast height is slow and erratic and is poorly related to site index (dominant height at 15 years breast-height age). The most precise model for computing heightߝgrowth curves was a Newnham constrained polymorphic expression (Newnham, R.M. 1988. A modification of the Ek-Payandeh nonlinear regression model for site-index curves. Can. J. For. Res. 18:115ߝ120) of the Ek nonlinear regression model (Ek, A.R. 1971. A formula for white spruce site-index curves. University of Wisconsin For. Res. Note 161. 2 p). Comparisons showed that site-index curves in North Central Ontario were comparable to site-index curves for white spruce plantations in southeastern Ontario. The first three to five internodes above 2.0 m gave the most precise estimates of site index based on growth intercepts. North. J. Appl. For. 23(4):257–263.

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

scholarly journals Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9421 ◽  
Author(s):  
Giovani L. Vasconcelos ◽  
Antônio M.S. Macêdo ◽  
Raydonal Ospina ◽  
Francisco A.G. Almeida ◽  
Gerson C. Duarte-Filho ◽  
...  
Keyword(s):  
Growth Model ◽  
Intervention Strategies ◽  
Analytic Formula ◽  
The Other ◽  
Cumulative Number ◽  
Window Of Opportunity ◽  
Intervention Model ◽  
Growth Regime ◽  
Johns Hopkins University ◽  
Richards Growth Model

The main objective of the present article is twofold: first, to model the fatality curves of the COVID-19 disease, as represented by the cumulative number of deaths as a function of time; and second, to use the corresponding mathematical model to study the effectiveness of possible intervention strategies. We applied the Richards growth model (RGM) to the COVID-19 fatality curves from several countries, where we used the data from the Johns Hopkins University database up to May 8, 2020. Countries selected for analysis with the RGM were China, France, Germany, Iran, Italy, South Korea, and Spain. The RGM was shown to describe very well the fatality curves of China, which is in a late stage of the COVID-19 outbreak, as well as of the other above countries, which supposedly are in the middle or towards the end of the outbreak at the time of this writing. We also analysed the case of Brazil, which is in an initial sub-exponential growth regime, and so we used the generalised growth model which is more appropriate for such cases. An analytic formula for the efficiency of intervention strategies within the context of the RGM is derived. Our findings show that there is only a narrow window of opportunity, after the onset of the epidemic, during which effective countermeasures can be taken. We applied our intervention model to the COVID-19 fatality curve of Italy of the outbreak to illustrate the effect of several possible interventions.

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