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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Main Author: Ali Jahani
Format: Article
Language:fas
Published: Research Institute of Forests and Rangelands of Iran 2016-06-01
Series:تحقیقات جنگل و صنوبر ایران
Subjects:
Online Access:http://ijfpr.areeo.ac.ir/article_106998_ac35e1881976ef73ec1352e52d5fe5c4.pdf
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spelling 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
collection DOAJ
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
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