Modeling of forest canopy density confusion in environmental assessment using artificial neural network
Environmental Impact Assessment (EIA) is well-known as a basic tool for environmental management and sustainable development. However, modelling approaches are generally preferred when quantitative entities are required for decision-making. The purpose of this study was to test artificial neural net...
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Research Institute of Forests and Rangelands of Iran
2016-06-01
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doaj-dcf9104a60724c9caf9f3c7fbec44c412020-11-25T01:37:06ZfasResearch Institute of Forests and Rangelands of Iranتحقیقات جنگل و صنوبر ایران1735-08832383-11462016-06-0124232231010.22092/ijfpr.2016.106998106998Modeling of forest canopy density confusion in environmental assessment using artificial neural networkAli Jahani0Assistant Prof., Department of Natural Environment and Biodiversity, Faculty of Environment, University of EnvironmentEnvironmental Impact Assessment (EIA) is well-known as a basic tool for environmental management and sustainable development. However, modelling approaches are generally preferred when quantitative entities are required for decision-making. The purpose of this study was to test artificial neural network incorporating ecosystem components, forest management activities and the forest canopy density confusion. The study area embraced three districts of Patom, Namkhaneh and Gorazbon within Khyroud research and educational forest of University of Tehran. Land Management Units were formed using available ecological databases and GIS. Based on qualitative and quantitative measures of ecological condition and human activities, the forest canopy density was simulated using artificial neural networks in Neuro Solutions ver. 5 software. Multilayer Perceptron network with one hidden layer and four neurons created the best function for optimizing topology with highest coefficient of determination ~ 0.9864. The results of sensitivity analysis revealed that human activities like the cattle density in land unit (ha), ecological and natural factors such as the average diameter of forest type trees and soil depth are associated with the highest effects on forest canopy density. As a conclusion, the impact assessment of implemented projects could offer an improved solution in decision making for similar projects across similar locations.http://ijfpr.areeo.ac.ir/article_106998_ac35e1881976ef73ec1352e52d5fe5c4.pdfEnvironmental Impact Assessmentforest canopy densityMultilayer Perceptronsensitivity analysisArtificial neural network |
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language |
fas |
format |
Article |
sources |
DOAJ |
author |
Ali Jahani |
spellingShingle |
Ali Jahani Modeling of forest canopy density confusion in environmental assessment using artificial neural network تحقیقات جنگل و صنوبر ایران Environmental Impact Assessment forest canopy density Multilayer Perceptron sensitivity analysis Artificial neural network |
author_facet |
Ali Jahani |
author_sort |
Ali Jahani |
title |
Modeling of forest canopy density confusion in environmental assessment using artificial neural network |
title_short |
Modeling of forest canopy density confusion in environmental assessment using artificial neural network |
title_full |
Modeling of forest canopy density confusion in environmental assessment using artificial neural network |
title_fullStr |
Modeling of forest canopy density confusion in environmental assessment using artificial neural network |
title_full_unstemmed |
Modeling of forest canopy density confusion in environmental assessment using artificial neural network |
title_sort |
modeling of forest canopy density confusion in environmental assessment using artificial neural network |
publisher |
Research Institute of Forests and Rangelands of Iran |
series |
تحقیقات جنگل و صنوبر ایران |
issn |
1735-0883 2383-1146 |
publishDate |
2016-06-01 |
description |
Environmental Impact Assessment (EIA) is well-known as a basic tool for environmental management and sustainable development. However, modelling approaches are generally preferred when quantitative entities are required for decision-making. The purpose of this study was to test artificial neural network incorporating ecosystem components, forest management activities and the forest canopy density confusion. The study area embraced three districts of Patom, Namkhaneh and Gorazbon within Khyroud research and educational forest of University of Tehran. Land Management Units were formed using available ecological databases and GIS. Based on qualitative and quantitative measures of ecological condition and human activities, the forest canopy density was simulated using artificial neural networks in Neuro Solutions ver. 5 software. Multilayer Perceptron network with one hidden layer and four neurons created the best function for optimizing topology with highest coefficient of determination ~ 0.9864. The results of sensitivity analysis revealed that human activities like the cattle density in land unit (ha), ecological and natural factors such as the average diameter of forest type trees and soil depth are associated with the highest effects on forest canopy density. As a conclusion, the impact assessment of implemented projects could offer an improved solution in decision making for similar projects across similar locations. |
topic |
Environmental Impact Assessment forest canopy density Multilayer Perceptron sensitivity analysis Artificial neural network |
url |
http://ijfpr.areeo.ac.ir/article_106998_ac35e1881976ef73ec1352e52d5fe5c4.pdf |
work_keys_str_mv |
AT alijahani modelingofforestcanopydensityconfusioninenvironmentalassessmentusingartificialneuralnetwork |
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