Monitoring soil carbon pool in the Hyrcanian coastal plain forest of Iran: Artificial neural network application in comparison with developing traditional models

Vahedi, A. A.

doi:10.1016/j.catena.2017.01.022

Cited by 26 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the 1970s the theory of geostatistics was initially applied to soil science, and used to study the spatial variability of soil properties [14][15][16]. Based on that research, many geostatistical techniques have been developed to predict the spatial variability of soil properties, such as ordinary kriging (OK) [6,17], cokriging [18,19], area-to-point kriging [20], inverse distance weighting (IDW) [21], artificial neural network method [22] and pedo-transfer functions [23]. Among them, OK has been most widely used [24,25].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability of Soil Physical Properties Based on GIS and Geo-Statistical Methods in the Red Beds of the Nanxiong Basin, China

Yan¹,

Hua²,

Yan³

et al. 2019

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Spatial variability of soil has an important influence on the structure and function of soil. The spatial distribution of soil physical properties provides basic and useful information relevant to soil management and ecological protection. A typical red beds basin was selected for this study, soil samples at 0-20 cm were taken from 150 locations in the northeast part of Nanxiong Basin, in which GIS and geostatistics were used to analyze the spatial variability of the soil physical properties. The results show that the coefficients of variation of soil bulk density, total porosity and capillary porosity are 9.82%, 4.47%, and 3.72%, respectively, which indicate weak variation. Pearson correlation indicated that soil bulk density was significantly positively correlated with soil moisture and capillary water capacity (p<0.01), with correlation coefficients of 0.85 and 0.91, respectively, but was significantly negatively correlated with total porosity, capillary porosity and non-capillary porosity, with correlation coefficients of 0.82, 0.71 and 0.94, respectively (p<0.01). The spatial distributions of soil physical properties using ordinary kriging (OK) and empirical bayesian kriging (EBK) methods were subjected to comparative analysis. In addition, different cross-validation indicators were applied to assess the performance of different interpolation methods. Cross-validation demonstrated that EBK performed better than OK. And EBK produced smaller regions of predicted soil physical properties than OK, highlighting the necessity of choosing the appropriate methods in studying the spatial distribution of soil properties.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatial Variability of Soil Physical Properties Based on GIS and Geo-Statistical Methods in the Red Beds of the Nanxiong Basin, China

Yan¹,

Hua²,

Yan³

et al. 2019

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…Artificial Neural Networks (ANNs) thus proved themselves to be a feasible technique in the BAI modeling strategies of A. angustifolia for the possibility of including different variables in the model and increasing the complexity of the relationship. This is possible because the ANN technique allows new variables to be included [57] based on biological theory and dynamic processes according to the ecological reality, and not on accidental or random correlations [58]. Furthermore, the good performance of the generated models in both training and validation, based on an appropriate structure (number of neurons, type of activation function, and input variables) indicates the stability of these models and their ability to present generalization.…”

Section: Discussionmentioning

confidence: 99%

Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

Barbosa

Costa

Schons

et al. 2022

Forests

View full text Add to dashboard Cite

This research aimed to develop statistical models to predict basal area increment (BAI) for Araucaria angustifolia using Artificial Neural Networks (ANNs). Tree species were measured for their biometric variables and identified at the species level. The data were subdivided into three groups: (1) intraspecific competition with A. angustifolia; (2) the first group of species that causes interspecific competition with A. angustifolia; and (3) the second group of species that causes interspecific competition with A. angustifolia. We calculated both the dependent and independent distance and the described competition indices, considering the impact of group stratification. Multi-layer Perceptron (MLP) ANN was structured for modeling. The main results were that: (i) the input variables size and competition were the most significant, allowing us to explain up to 77% of the A. angustifolia BAI variations; (ii) the spatialization of the competing trees contributed significantly to the representation of the competitive status; (iii) the separate variables for each competition group improved the performance of the models; and (iv) besides the intraspecific competition, the interspecific competition also proved to be important to consider. The ANN developed showed precision and generalization, suggesting it could describe the increment of a species common in native forests in Southern Brazil and with potential for upcoming forest management initiatives.

show abstract

“…The high r yŷ of ANN retained in both scenarios was due to their ability to model complex relationships between qualitative and quantitative variables [47][48][49]. The largest neuron number in the hidden layer for the general data was due to the modeling complexity with qualitative variables (region, farm, and groups of clones) [50].…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Júnior

Rocha

Ebling

et al. 2019

Forests

View full text Add to dashboard Cite

Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).

show abstract

Monitoring soil carbon pool in the Hyrcanian coastal plain forest of Iran: Artificial neural network application in comparison with developing traditional models

Cited by 26 publications

References 16 publications

Spatial Variability of Soil Physical Properties Based on GIS and Geo-Statistical Methods in the Red Beds of the Nanxiong Basin, China

Spatial Variability of Soil Physical Properties Based on GIS and Geo-Statistical Methods in the Red Beds of the Nanxiong Basin, China

Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Contact Info

Product

Resources

About