Dynamic prediction of uneven-aged natural forest for yield of Pinus taiwanensis using joint modelling

Hua, Wei; Ye, Hongmeng; Rau, Jui-Yeh; Qiu, Tian

doi:10.1007/s10661-020-8204-7

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Meng Xianyu [21] studied the age structure of natural Xing'an larch forests and found that the age structure of stands presents a single-peak mountain curve, which is more complex than that of plantations. Hua Weiping [22] built a dynamic prediction model of the volume of natural stands of Pinus taiwanensis by replacing the age of stands with intervals, with a correlation coefficient of 0.970. The fitting effect is higher than the goodness-of-fit value of the stand volume prediction model established in this study.…”

Section: Discussionmentioning

confidence: 99%

Developing Growth and Harvest Prediction Models for Mixed Coniferous and Broad-Leaved Forests at Different Ages

Hua

Pan

Zhu

et al. 2023

Forests

Self Cite

View full text Add to dashboard Cite

In order to clarify the combined impact of tree species composition, site quality, and stand age on the growth and harvest of mixed forests, the prediction models of average DBH and stand volume for mixed forests were established, respectively. The interval period and tree species composition coefficient (TSCC) were considered as independent variables. These models were then optimized by using the particle swarm optimization algorithm for reparameterization and evaluating their applicability. It was found that after introducing the site quality grade and TSCC, the average stand height prediction model showed a better fitting result. The fit accuracy of the average DBH prediction model and the stand volume prediction model were both improved with the help of the TSCC, mainly because the tree species composition affects the growth rate of the average stand height and average DBH and the maximum growth rate of the stand volume. The degree of the impact can be sorted as Cunninghamia lanceolata > Pinus massoniana > hard broad-leaved tree species (group). Overall, the established growth and harvest prediction models for mixed forests with the interval period and TSCC as independent variables have high fit accuracy and applicability.

show abstract

Section: Discussionmentioning

confidence: 99%

Developing Growth and Harvest Prediction Models for Mixed Coniferous and Broad-Leaved Forests at Different Ages

Hua

Pan

Zhu

et al. 2023

Forests

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hook., Larix gmelinii (Rupr.) Kuzen., Platycladus orientalis (L.) Franco, Pinus taiwanensis Hayata, Robinia pseudoacacia L. and Populus tomentosa Carrière (Zuyuan and Xinyou 1989;Changxiong 1995;Shouzheng and Xifei 2012;Wei-Wei and Feng-Ri 2008;Hua et al 2020). Still, for most of the dominant species and major forest regions the growth and yield models are not available.…”

Section: Introductionmentioning

confidence: 99%

Two subject specific modelling approaches to construct base-age invariant polymorphic site index curves with varying asymptotes

Sprengel¹,

Spiecker²,

Wu³

2022

Silva Fenn.

View full text Add to dashboard Cite

For constructing growth and yield models the concept of site index as measure of productivity is crucial. Here, we use nonlinear mixed-effects models (NLME) with random individual effects and nonlinear models with dummy variables as fixed individual effects (NLFE) to fit mechanistic growth functions to stem analysis data of the economically most important tree species in Zhongtiaoshan forest region, China. The Richards and Lundqvist function are formulated into five dynamic equations (R1, R2, L1, L2 and L3) applying the generalized algebraic difference approach (GADA), which inherit polymorphism, varying asymptotes and base-age invariance. According to Akaike information criterion the R1 model as NLFE fits height growth data of CarriÃ¨re, Franch., Koidz., Blume and Sukaczev best, while for Blume R2 as NLFE fits height growth data best. For Mayr L1 as NLME has been selected as best model, as R1 and R2 both as NLFE and NLME are not extrapolating the comparably short length of height growth data well enough. However, according to the root mean square error and bias differences between model fits of both the selected equation and the chosen model fitting approach are not so clear. Presented families of height growth curves serve as planning tools to identify site index and therefore assess productivity of forest stands in the studied region. A direct comparison of the productivity of forest stands of the same tree species is possible due to base-age invariance of the selected models.Pinus tabuliformisPinus armandiiQuercus liaotungensisQuercus alienaBetula platyphyllaQuercus variabilisLarix principis-rupprechtii

show abstract

Clustering and Prediction Analysis of the Coordinated Development of China’s Regional Economy Based on Immune Genetic Algorithm

Yang

2021

Complexity

View full text Add to dashboard Cite

Since the opening of the economy, China’s regional economy has developed rapidly, the overall national strength has been increasing, and the people’s living standards have been continuously improved. The issue of coordinated regional development has become an important issue in today’s society. Genetic algorithm is a kind of prediction algorithm that has developed rapidly in recent years and is widely used. However, when solving engineering prediction problems, there are often problems such as premature convergence and easiness to fall into local optimal solutions. Therefore, on the basis of studying the related theories of genetic algorithm and artificial immune algorithm, this paper uses the advantages of the two algorithms, combines the two algorithms, and proposes an improved algorithm for genetic algorithm-adaptive immune genetic algorithm. Taking genetic algorithm as the basic framework, the operators and selection methods of artificial immune algorithm are integrated. Using the adaptive concept, the formulas of adaptive crossover probability and mutation probability are innovatively designed. Compared with the fixed value of the immune genetic algorithm, the introduction of the adaptive concept can intelligently adjust the optimization process and increase the optimization speed. Considering the double uncertain factors of product market demand and waste product recycling in the remanufacturing supply chain system, the maximization of logistics network operating profit, the minimization of environmental impact, and the maximization of customer satisfaction are the forecast goals. The market demand of uncertain products is effectively controlled through the option contract mechanism, and a multiobjective forecasting model based on the option contract mechanism is established. According to the characteristics of the model, an improved immune genetic algorithm is designed to solve the problem, and the effectiveness of the immune genetic algorithm is verified through an example.

show abstract

Dynamic prediction of uneven-aged natural forest for yield of Pinus taiwanensis using joint modelling

Cited by 3 publications

References 15 publications

Developing Growth and Harvest Prediction Models for Mixed Coniferous and Broad-Leaved Forests at Different Ages

Developing Growth and Harvest Prediction Models for Mixed Coniferous and Broad-Leaved Forests at Different Ages

Two subject specific modelling approaches to construct base-age invariant polymorphic site index curves with varying asymptotes

Clustering and Prediction Analysis of the Coordinated Development of China’s Regional Economy Based on Immune Genetic Algorithm

Contact Info

Product

Resources

About