A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method
The study presents models of error estimation in trees’ directional felling according to several effective factors using the subtractive clustering in the Adaptive Neuro-Fuzzy Inference System. A total number of 95 trees in the compartment 207 of 2nd district of Nav watershed in Guilan province were...
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| Format: | Article |
| Language: | fas |
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Research Institute of Forests and Rangelands of Iran
2016-03-01
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| Series: | تحقیقات جنگل و صنوبر ایران |
| Subjects: | |
| Online Access: | http://ijfpr.areeo.ac.ir/article_106689_095d0e9c4a2d30687456c03582f3710f.pdf |
| _version_ | 1819012061102866432 |
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| author | Esmaeil Ghajar Ramin Naghdi Mehrdad Nikooy |
| author_facet | Esmaeil Ghajar Ramin Naghdi Mehrdad Nikooy |
| author_sort | Esmaeil Ghajar |
| collection | DOAJ |
| description | The study presents models of error estimation in trees’ directional felling according to several effective factors using the subtractive clustering in the Adaptive Neuro-Fuzzy Inference System. A total number of 95 trees in the compartment 207 of 2nd district of Nav watershed in Guilan province were felled by felling group and regardless to the group’s skill, using manual chainsaw. The difference between predicted and real falling direction of trees was measured as felling error. To generate models, twelve independent variables were assumed to be the effective factors, and the two types of learning algorithm (LA), two inference types (IT) and five types of membership function (MF) for input variables were applied through the subtractive clustering method in the ANFIS. Results indicated that the trapezoidal type of MF in combination with the first-order type of Sugeno IT and the back propagation LA had the best performance among all combinations of setting parameters. The sensitivity analysis of the optimal model showed that the model was very sensitive to the changes in terrain slope, the angles of backcut and undercut surfaces and DBH, respectively. Results also revealed that felling group properly predicted the fall direction and performed the directional felling in the steeper terrain. In addition, the increase of DBH and opening too much the undercut notch have accompanied with the increase of felling error. |
| first_indexed | 2024-12-21T01:38:03Z |
| format | Article |
| id | doaj.art-09133a2a071940f4bb42aeeb559d4ddf |
| institution | Directory Open Access Journal |
| issn | 1735-0883 2383-1146 |
| language | fas |
| last_indexed | 2024-12-21T01:38:03Z |
| publishDate | 2016-03-01 |
| publisher | Research Institute of Forests and Rangelands of Iran |
| record_format | Article |
| series | تحقیقات جنگل و صنوبر ایران |
| spelling | doaj.art-09133a2a071940f4bb42aeeb559d4ddf2022-12-21T19:20:13ZfasResearch Institute of Forests and Rangelands of Iranتحقیقات جنگل و صنوبر ایران1735-08832383-11462016-03-01241766510.22092/ijfpr.2016.106689106689A neuro-fuzzy model of error in directional felling operation using the subtractive clustering methodEsmaeil Ghajar0Ramin Naghdi1Mehrdad Nikooy2Assistant Prof., Department of Forestry, Faculty of Natural Resources, University of GuilanAssociate Prof., Department of Forestry, Faculty of Natural Resources, University of GuilanAssistant Prof., Department of Forestry, Faculty of Natural Resources, University of GuilanThe study presents models of error estimation in trees’ directional felling according to several effective factors using the subtractive clustering in the Adaptive Neuro-Fuzzy Inference System. A total number of 95 trees in the compartment 207 of 2nd district of Nav watershed in Guilan province were felled by felling group and regardless to the group’s skill, using manual chainsaw. The difference between predicted and real falling direction of trees was measured as felling error. To generate models, twelve independent variables were assumed to be the effective factors, and the two types of learning algorithm (LA), two inference types (IT) and five types of membership function (MF) for input variables were applied through the subtractive clustering method in the ANFIS. Results indicated that the trapezoidal type of MF in combination with the first-order type of Sugeno IT and the back propagation LA had the best performance among all combinations of setting parameters. The sensitivity analysis of the optimal model showed that the model was very sensitive to the changes in terrain slope, the angles of backcut and undercut surfaces and DBH, respectively. Results also revealed that felling group properly predicted the fall direction and performed the directional felling in the steeper terrain. In addition, the increase of DBH and opening too much the undercut notch have accompanied with the increase of felling error.http://ijfpr.areeo.ac.ir/article_106689_095d0e9c4a2d30687456c03582f3710f.pdfchainsawmembership functionSugenoANFIScuttingsoft computing |
| spellingShingle | Esmaeil Ghajar Ramin Naghdi Mehrdad Nikooy A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method تحقیقات جنگل و صنوبر ایران chainsaw membership function Sugeno ANFIS cutting soft computing |
| title | A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method |
| title_full | A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method |
| title_fullStr | A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method |
| title_full_unstemmed | A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method |
| title_short | A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method |
| title_sort | neuro fuzzy model of error in directional felling operation using the subtractive clustering method |
| topic | chainsaw membership function Sugeno ANFIS cutting soft computing |
| url | http://ijfpr.areeo.ac.ir/article_106689_095d0e9c4a2d30687456c03582f3710f.pdf |
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