Artificial neural network application in comparison with modeling allometric equations for predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran

No abstract is available for this paper.

• Publication year

  2016

• Venue

  Biomass & Bioenergy

• Publication date

  2016-05-01

• Fields of study

  Chemistry, Engineering, Environmental Science

• Identifiers
  DOI 10.1016/J.BIOMBIOE.2016.03.020
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Database of 478 allometric equations to estimate biomass for Mexican trees and forests

  2015cited by this paper
• Should tree biomass allometry be restricted to power models

  2015cited by this paper
• Comparison of regression and artificial neural network models for estimation of global solar radiations

  2015cited by this paper
• An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model

  2014cited by this paper
• A critical review of forest biomass estimation models, common mistakes and corrective measures

  2014cited by this paper
• The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions

  2013cited by this paper
• GlobAllomeTree: international platform for tree allometric equations to support volume, biomass and carbon assessment

  2013cited by this paper
• Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia

  2012cited by this paper
• Application of Artificial Neural Network in the Modeling of Skidding Time Prediction

  2011cited by this paper
• Above- and belowground biomass in a Brazilian Cerrado

  2011cited by this paper
• Application of artificial neural networks in global climate change and ecological research: An overview

  2010cited by this paper
• Estimation of thermophysical properties of dimethyl ether as a commercial refrigerant based on artificial neural networks

  2010cited by this paper
• Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa

  2010cited by this paper
• Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China

  2010cited by this paper
• Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa

  2010cited by this paper
• Estimating tree bole volume using artificial neural network models for four species in Turkey.

  2010cited by this paper
• Darwin and Huxley revisited: the origin of allometry

  2009cited by this paper
• Partitioning rainfall into throughfall, stemflow, and interception loss in an oriental beech (Fagus orientalis Lipsky) forest during the growing season.

  2009cited by this paper
• Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests

  2009cited by this paper
• Tree allometry and improved estimation of carbon stocks and balance in tropical forests

  2005cited by this paper
• Allometric relationships of different tree species and stand above ground biomass in the Gomera laurel forest (Canary Islands)

  2005cited by this paper
• Evaluating tree carbon predictions for beech (Fagus sylvatica L.) in western Germany

  2004cited by this paper
• Aboveground biomass relationships for beech (Fagus moesiaca Cz.) trees in Vermio Mountain, Northern Greece, and generalised equations for Fagus sp.

  2003cited by this paper
• Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests

  2001cited by this paper
• Biomass allocation in old‐field annual species grown in elevated CO2 environments: no evidence for optimal partitioning

  2000cited by this paper
• Impacts of size and competition on tree form and distribution of aboveground biomass in Scots pine

  1998cited by this paper
• Nonlinear Regression Analysis and Its Applications

  1988cited by this paper
• Distribution and yield of biomass from young Pinus sylvestris and Picea abies stands on drained and fertilized peatland

  1986cited by this paper
• Biomass and Production of Trees in a Mixed Deciduous Woodland: I. Girth and Height as Parameters for the Estimation of Tree Dry Weight

  1968cited by this paper
• Die Abhängigkeit des Hirngewichtes von dem Körpergewicht und den geistigen Fähigkeiten

  1892cited by this paper

• A novel UAV lidar-derived shrub structural index for estimating above-ground biomass

  2026cites this paper
• Improving the Efficiency of Allometric Equations Using Artificial Neural Networks in Coppicing Stands of Brant's Oak

  2025cites this paper
• Research on the Flow Characteristics and Collection Optimization of Two-Phase Flow of Heavy Oil and Hot Water

  2025cites this paper
• Biomass Estimation of Mahogany (Swietenia macrophylla): An Optimized Weighted Ensemble-based Machine Learning Approach

  2025cites this paper
• Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests

  2024cites this paper
• Comparing the allometric model to machine learning algorithms for aboveground biomass estimation in tropical forests

  2024cites this paper
• Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland

  2024cites this paper
• Artificial Neural Network and Remote Sensing combined to predict the Aboveground Biomass in the Cerrado biome.

  2024cites this paper
• Modeling Height–Diameter Relationship Using Artificial Neural Networks for Durango Pine (Pinus durangensis Martínez) Species in Mexico

  2023cites this paper
• Estimating Forest Aboveground Biomass Combining Pléiades Satellite Imagery and Field Inventory Data in the Peak–Cluster Karst Region of Southwestern China

  2023cites this paper
• Developing machine learning models with multiple environmental data to predict stand biomass in natural coniferous-broad leaved mixed forests in Jilin Province of China

  2023cites this paper
• Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

  2023cites this paper
• The relationship between plant diversity and aboveground biomass in managed and unmanaged temperate forests

  2023cites this paper
• Random forest method for analysis of remote sensing inversion of aboveground biomass and grazing intensity of grasslands in Inner Mongolia, China

  2023cites this paper
• Allometric Models and Biomass Conversion and Expansion Factors to Predict Total Tree-level Aboveground Biomass for Three Conifers Species in Iran

  2023cites this paper
• Predicting stem taper using artificial neural network and regression models for Scots pine (Pinus sylvestris L.) in northwestern Türkiye

  2023cites this paper
• Improving 3-PG calibration and parameterization using artificial neural networks

  2023cites this paper
• Biomass, Stem Volume, and Carbon Sequestration Based on Allometric Equations for Populus deltoides W. Bartram ex Marshall. Planta- tions (A Study of Guilan, Iran)

  2023cites this paper
• MACHINE LEARNING PREDICTION OF TROPICAL FOREST ABOVEGROUND BIOMASS ESTIMATION

  2023cites this paper
• LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations

  2023cites this paper
• Analysis of various crown variables on stem form for Cunninghamia lanceolata based on ANN and taper function

  2022cites this paper
• Decoupled abiotic and biotic drivers of aboveground and topsoil organic carbon stocks in temperate forests

  2022cites this paper
• Machine learning models for estimating above ground biomass of fast growing trees

  2022cites this paper
• Productivity-Based Land Suitability and Management Sensitivity Analysis: The Eucalyptus E. urophylla × E. grandis Case

  2022cites this paper
• Response of established oriental beech (Fagus orientalis L.) regeneration to canopy gap formation in the Hyrcanian forest ecosystems

  2022cites this paper
• A novel method for approaching the compatibility of tree biomass estimation by multi-task neural networks

  2022cites this paper
• Simulated annealing in feature selection approach for modeling aboveground carbon stock at the transition between Brazilian Savanna and Atlantic Forest biomes

  2022cites this paper
• Resource requirements for ecosystem conservation: A combined industrial and natural ecology approach to quantifying natural capital use in nature

  2022cites this paper
• Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

  2022cites this paper
• Artificial neural networks and remote sensing for volumetric prediction in a Eucalyptus sp. plantation

  2021cites this paper
• Remote-sensing inversion method for aboveground biomass of typical steppe in Inner Mongolia, China

  2021cites this paper
• Variation in carbon traits among Fraxinus mandshurica populations and allometric equations between carbon traits and growth traits

  2021cites this paper
• Tree-size dimension inequality shapes aboveground carbon stock across temperate forest strata along environmental gradients

  2021cites this paper
• Multi-objective centralization-decentralization trade-off analysis for multi-source renewable electricity generation expansion planning: A case study of Iran

  2021cites this paper
• Modeling of Iranian oak Regeneration in Zagros Forests using Artificial Neural Network Algorithm

  2021cites this paper
• Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison Between Regression and Artificial Neural Networks

  2020influential citation
• Environmental filtering, predominance of strong competitor trees and exclusion of moderate-weak competitor trees shape species richness and biomass.

  2020cites this paper
• Big-trees – Energy mechanism underlies forest diversity and aboveground biomass

  2020cites this paper
• Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurally-complex forests.

  2020cites this paper
• Machine learning approach for estimating tree volume

  2020cites this paper
• Machine learning: Modeling increment in diameter of individual trees on Atlantic Forest fragments

  2020cites this paper
• Different approaches for modeling Swietenia macrophylla commercial volume in an Amazon agroforestry system

  2019cites this paper
• Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change.

  2019cites this paper
• ARTIFICIAL NEURAL NETWORKS AND LINEAR REGRESSION REDUCE SAMPLE INTENSITY TO PREDICT THE COMMERCIAL VOLUME OF EUCALYPTUS CLONES

  2019cites this paper
• Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data

  2019cites this paper
• Eco-efficiency assessment of bioelectricity production from Iranian vineyard biomass gasification

  2019cites this paper
• Evaluation of potential modeling approaches for Scots pine stem diameter prediction in north-eastern Turkey

  2019cites this paper
• Prognoses of diameter and height of trees of eucalyptus using artificial intelligence.

  2018cites this paper
• Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region

  2018cites this paper
• Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province, Southwestern China

  2018cites this paper
• STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY

  2018cites this paper
• Estimating Spartina alterniflora fractional vegetation cover and aboveground biomass in a coastal wetland using SPOT6 satellite and UAV data

  2018cites this paper
• Application of machine-learning methods in forest ecology: recent progress and future challenges

  2018cites this paper
• Tree-bark volume prediction via machine learning: A case study based on black alder's tree-bark production

  2018cites this paper
• Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil.

  2018cites this paper
• Comprehensive Evaluation of Machine Learning Techniques for Estimating the Responses of Carbon Fluxes to Climatic Forces in Different Terrestrial Ecosystems

  2018cites this paper
• Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation.

  2018cites this paper
• An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India

  2017cites this paper
• Artificial Neural Network Models: An Alternative Approach for Reliable Aboveground Pine Tree Biomass Prediction

  2017cites this paper
• Monitoring soil carbon pool in the Hyrcanian coastal plain forest of Iran: Artificial neural network application in comparison with developing traditional models

  2017cites this paper
• Horizontal and vertical distribution of carbon stock in natural stands of Hyrcanian lowland forests: A case study, Nour Forest Park, Iran

  2016cites this paper
• Biomass allocation patterns and allometric relationships between components of the androdioecious Acer tegmentosum

  2016cites this paper
• Selection of input vectors for estimation of aboveground biomass of Mimosa scabrella Benth. using an artificial neural network

  2016influential citation