Employing artificial neural network for effective biomass prediction: An alternative approach

Güner, Şükrü Teoman; Diamantopoulou, Maria J.; Poudel, Krishna P.; Çömez, Aydın; Özçelık, Ramazan

doi:10.1016/j.compag.2021.106596

Cited by 20 publications

(14 citation statements)

References 48 publications

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“…The volume-derived biomass models use the stand volume as a single predictor, while multiplying the obtained stand volume with biomass expansion factors allows for the calculation of a stand's total or component biomass [20][21][22]. The establishment of additive individual tree biomass models in different regions and stand conditions worldwide has been crucial in supporting the development of stand biomass models [23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

Xin

Shahzad²,

Mahardika

et al. 2023

Forests

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Accurate large-scale biomass prediction is crucial for assessing forest carbon storage and dynamics. It can also inform sustainable forest management practices and climate change mitigation efforts. However, stand-level biomass models are still scarce worldwide. Our study aims to introduce the generalized additive model (GAM) as a convenient and efficient approach for forest biomass estimation. Data from 311 sample plots of three conifer species in northeastern China were used to evaluate the performance of the GAM model and compare it with traditional nonlinear seemingly unrelated regression (NSUR) models in predicting stand biomass, including total, aboveground, and component biomass. The results indicated that the goodness of fit of GAM was better than that of NSUR in two model systems. In the majority of cases, the scatter plots and prediction performance revealed that the stand total and component biomass models utilizing GAM outperformed those based on NSUR. Disregarding heteroscedasticity and requiring fewer statistical assumptions provide additional support for the replacement of NSUR-based models with GAM-based models. This study implies that the GAM approach has greater potential for developing a system of stand biomass models.

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Section: Introductionmentioning

confidence: 99%

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

Xin

Shahzad²,

Mahardika

et al. 2023

Forests

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“…However, the current information on tree biomass estimates is not sufficient for the preparation of management plans for complex forest ecosystems in Türkiye. In this country, aboveground biomass estimation equations for the whole tree and its components have been developed for some tree species at the regional level [10][11][12][13][14][15][16][17][18][19]. Except for Özçelik et al [10] and Güner et al [19], the estimations generally utilized linear or nonlinear traditional regression equations with one or more independent variables.…”

Section: Introductionmentioning

confidence: 99%

“…In this country, aboveground biomass estimation equations for the whole tree and its components have been developed for some tree species at the regional level [10][11][12][13][14][15][16][17][18][19]. Except for Özçelik et al [10] and Güner et al [19], the estimations generally utilized linear or nonlinear traditional regression equations with one or more independent variables. However, when separate biomass equations are developed for all components of a tree (stem, branches, bark, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Traditional regression modeling needs much effort to be spent on the regression assumptions examination along with the selection of the optimal form of a function. Previous studies [10,19] have shown that artificial neural networks (ANNs), part of the scientific area of machine learning, are worth more exploration since these systems have shown their potential to overcome the aforementioned difficulties. As powerful nonparametric machine learning techniques included in the artificial intelligence methods, the structure of ANNs has been thoroughly described and discussed [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

Kalkanlı Genç,

Diamantopoulou,

Özçelik

2023

Forests

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Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling.

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“…In addition, the need of the specifi cation of the appropriate form of the regression model that can accurately describe the data in hand, is a diffi cult and time-consuming requirement that has to be successfully addressed. For these reasons, the forest scientifi c research has focused on the application of new modeling methods, such as that of artifi cial intelligence systems (Artifi cial Intelligence, AI) and their comparative evaluation with the more classic modeling methods that were widely used and are still used today, such as the theory of regression analysis, (DIAMANTOPOULOU, 2005;DIA-MANTOPOULOU et al, 2009;DIAMANTOPOULOU et al, 2018;ÖZÇELIK et al, 2019;BAYAT et al, 2020;BOROUGHANI et al, 2022;GÜNER et al, 2022), in order to determine their usefulness in solving problems of forest research.…”

Section: Introductionmentioning

confidence: 99%

Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

Diamantopoulou

2022

Folia Oecologica

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The difficulty of locating and measuring the over-bark tree bole diameters at heights that are far from the ground, is a serious problem in ground-truth data measurements in the field. This problem could be addressed through the application of intelligent systems methods. The paper explores the possibility of applying the Random Forest regression method (RFr) in order to assess, as accurately as possible, the size of the tree bole diameters at any height above the ground, considering data that can be easily measured in the field. For this purpose, diameter measurements of pine trees (Pinus brutia Ten.) from the Seich–Sou urban forest of Thessaloniki, Greece, were used. The effectiveness of the Random Forest regression technique is compared with the results of non-linear regression models that fitted to the available data and evaluated. This research has shown that the RFr method can be a reliable alternative methodology in order to receive accurate information provided by the model, saving time and effort in field.

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Employing artificial neural network for effective biomass prediction: An alternative approach

Cited by 20 publications

References 48 publications

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

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