Mauno Pesonen, Arto Kettunen, Petri Räsänen. (1995). Modelling non-industrial private forest landowners’ strategic decision making by using logistic regression and neural networks: Case of predicting the choice of forest taxation basis. Silva Fennica vol. 29 no. 2 article id 5555. https://doi.org/10.14214/sf.a9206

Keywords: logistic regression; Finland; Neural Networks; forest owners; forest taxation; non-industrial private forest landowners,; timber management strategies

Abstract | View details | Full text in PDF | Author Info

In this study, logistic regression and neural networks were used to predict non-industrial private forests (NIPF) landowners’ choice of forest taxation basis. The main frame of reference of the study was the Finnish capital taxation reform of 1993. As a consequence of the reform, landowners were required to choose whether to be taxed according to site-productivity or realized-income during the coming transition period of thirteen years.

The most important factor affecting the landowners’ choice of taxation basis was the harvest rate during the transition period, i.e. the chosen timber management strategy. Furthermore, the estimated personal marginal tax rate and the intention to cut timber during next three years affected the choice. The descriptive landowner variables did not have any marked effect on the choice of forest taxation basis.

On average, logistic regression predicted 71% of the choices correctly; the corresponding figure for neural networks was 63%. In both methods, the choice of site-productivity taxation was predicted more accurately than the choice of realized-income taxation. An increase in the number of model variable did not significantly improve the results of neural networks and logistic regression.

Pesonen, E-mail: mp@mm.unknown
Kettunen, E-mail: ak@mm.unknown
Räsänen, E-mail: pr@mm.unknown

Category : Research article

article id 460, category Research article

Hong Ling, Sandhya Samarasinghe, G. Don Kulasiri. (2009). Modelling variability in full-field displacement profiles and Poisson ratio of wood in compression using stochastic neural networks. Silva Fennica vol. 43 no. 5 article id 460. https://doi.org/10.14214/sf.460

Keywords: wood; digital image correlation; displacement profiles; variability; micro structure; stochastic neural networks; Poisson ratio

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Vertical and horizontal displacement profiles in compression parallel-to-grain in a 20 x 20 mm area (30 x 21 or 630 points) in the Tangential–Longitudinal (T–L) and Radial Longitudinal (R–L) sections of small wood columns were obtained from digital image correlation applied to simultaneously captured images of the two surfaces. These consisted of 21 displacement realisations of 30 points along the length of the specimen. They revealed considerable local variations. Stochastic neural networks were successfully developed to simulate trends and noise across and along a specimen in both displacements as well as Poisson ratios in T–L and R–L sections for two selected load levels of 20kN and 40kN. These networks specifically embed noise characteristics extracted from data to generate realistic displacement and Poisson ratio realisations with inherent variability. Models were successfully validated using independent data extracted based on bootstrapping method with high accuracy with R² ranging from 0.79 to 0.91. The models were further validated successfully using a second approach involving Confidence Intervals generated from the data extracted from the models. Models and experimental results revealed that for 20kN load, both vertical and horizontal displacements in T–L section were less heterogeneous across the specimen (smaller vertical shearing and horizontal strain, respectively) than those in the R–L section. For the 40kN load, both displacement profiles in the T–L section were less noisy and more compact than those for the 20kN load indicating less heterogeneity due to compaction of structure. In the R–L section, larger vertical shearing and horizontal strains persisted at 40 kN load. Poisson ratio decreased with load and it was nonlinear in both sections but T–L section showed much less noise across the specimen than the R–L section.

Ling, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand E-mail: hl@nn.nz
Samarasinghe, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand E-mail: sandhya.samarasinghe@lincoln.ac.nz
Kulasiri, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand E-mail: gdk@nn.nz

article id 309, category Research article

Sandhya Samarasinghe, Don Kulasiri, Tristan Jamieson. (2007). Neural networks for predicting fracture toughness of individual wood samples. Silva Fennica vol. 41 no. 1 article id 309. https://doi.org/10.14214/sf.309

Keywords: Pinus radiata; New Zealand; video imaging; strain energy release rate; Neural Networks; fracture toughness

Abstract | View details | Full text in PDF | Author Info

Strain energy release rate (G_Ic) of Pinus radiata in the TL opening mode was determined using the compliance crack length relationship. A total of 123 specimens consisting of four sizes of specimen with each size having four different crack lengths were tested. For each specimen, grain and ring angles, density and moisture content were measured. Video imaging, was used to measure crack length during propagation. Since cracks extended in stages, full compliance-crack length relationship was developed for each specimen based on their initial and subsequent crack lengths. No significant differences in G_Ic, between initial and subsequent crack lengths were found for the smaller specimens by paired sample t-tests, but differences were significant for the largest specimen size. The Average fracture toughness was calculated from G_Ic and it was 215 kPa.m^0.5. Three artificial neural networks were developed to predict the: 1) force required to propagate a crack, 2) crack extension, and 3) fracture toughness of an individual specimen. Each was successful, producing respective R² of 0.870, 0.865, and 0.621 on validation data. A sensitivity analysis of the networks revealed that the crack length was the most influential with 21% contribution followed by grain angle with 14% contribution for predicting the applied force. This was followed by volume and physical properties. For predicting the crack extension, density had the greatest contribution (20%) followed by previous crack length and force contributing 16% equally. Fracture toughness was dominated by the dimensional parameters of the specimen contributing (42%) followed by anisotropy and physical properties.

Samarasinghe, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: ss@nn.nz
Kulasiri, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: dk@nn.nz
Jamieson, Centre for Advanced Computational Solutions (C-fACS), Lincoln University, New Zealand E-mail: tj@nn.nz

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