Related Links
A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method | ||
| تحقیقات جنگل و صنوبر ایران | ||
| Article 8, Volume 24, Issue 1 - Serial Number 63, March 2016, Pages 76-65 PDF (1.04 M) | ||
| Document Type: Research article | ||
| DOI: 10.22092/ijfpr.2016.106689 | ||
| Authors | ||
| Esmaeil Ghajar* 1; Ramin Naghdi2; Mehrdad Nikooy3 | ||
| 1Assistant Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan | ||
| 2Associate Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan | ||
| 3Assistant Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| chainsaw; membership function; Sugeno; ANFIS; cutting; soft computing | ||
| References | ||
|
- Andrews, R., Diederich, J. and Tickle, A., 1995. A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Base Systems, 8: 373-389. - Appanah, S. and Weinland, G., 1990. Will the management systems for hill dipterocarp forests stand up?. Journal of Tropical Forest Science: 3(2): 140-158. - Behjou, F.K., Majnounian, B., Dvorak, J., Namiranian, M., Saeed, A. and Feghhi, J., 2009. Productivity and cost of manual felling with chainsaw in Caspian forests. Journal of Forest Science, 55(2): 96-100. - Bermejo, S. and Cabestany, J., 2001. Oriented principal component analysis for large margin classifiers. Neural Networks, 14(10): 1447-1461. - Brock, S.M., Jones, K.D. and Miller, G.W., 1986. Felling and skidding costs associated with thinning a commercial Appalachian hardwood stand in northern West Virginia. Northern Journal of Applied Forestry, 3: 159-163. - Cedergren, J., Falck, J., Garcia, A., Goh, F. and Hagner M., 2002. Feasibility and usefulness of directional felling in a tropical rain forest. Journal of Tropical Forest Science, 14(2): 179-190. - Conway, S., 1982. Logging Practices: Principles of Timber Harvesting Systems. Miller Freeman, San Francisco, 416p. - Ershadifar, M., Nikooy, M. and Naghdi, R., 2011. Ability assessment of felling crew in directional felling in west forest of Guilan province. Iranian Journal of Forest, 3(2): 169-176 (In Persian). - FAO, 2004. Reduced Impact Logging in Tropical Forests: Literature Synthesis, Analysis and Prototype Statistical Framework. Forest Harvesting and Engineering Program, Rome. - Froese, K. and Han, H.S., 2006. Residual stand damage from cut-to-length thinning of a mixed conifer stand in northern Idaho. Western Journal of Applied Forestry, 21(3): 142-148. - Ghajar, I. and Najafi, A., 2014. Modeling the landslide susceptibility of a mountain forest using adaptive neuro-fuzzy inference system (ANFIS) for forest road planning. Iranian Journal of Forest and Poplar Research, 22(3): 509-526 (In Persian). - Ghajar, I., Najafi, A., Torabi, S.A., Khamehchiyan, M. and Boston, K., 2012. An adaptive network-based fuzzy inference system for rock share estimation in forest road construction. Croatian Journal of Forest Engineering, 33(2): 313-328. - Holmes, T.P., Blat, G.M., Zweede, J.C., Pereira, R., Barreto, P., Boltz, F. and Bauch, R., 2002. Financial and ecological indicators of reduced impact logging performance in the eastern Amazon. Forest Ecology and Management, 163: 93-110. - Jang, J.S., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3): 665-685. - Karaman, A. and Caliskan, E., 2009. Affective factors weight estimation in tree felling time by artificial neural networks. Expert Systems with Applications, 36: 4491-4496. - Kermani, B.G., 2007. Modeling oligonucleotide probes for SNP genotyping assays using an adaptive neuro-fuzzy inference system. Sensors and Actuators, 121: 462-468. - Krueger, W., 2004. Effects of future tree flagging and skid trail planning on conventional diameter-limit logging in a Bolivian tropical forest. Forest Ecology and Management, 188: 381–393. - Lortz, D., Kluender, R., McCoy, W., Stokes, B. and Klepac J., 1997. Manual felling time and productivity in southern forests. Forest Products Journal, 47: 59-63. - Lotfalian, M., 2012. Logging. Published by Ayiizh, Tehran, 488p (In Persian). - Magagnotti N. and Spinelli, R., 2012. Good Practice Guidelines for Biomass Production Studies. CVR Ivalsa, Sesto Fiorentino, 50p. - Mamdani, E.H. and Assilian, S., 1974. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1): 1-13. - Naghdi, R., Bagheri, I., Taheri Abkenar, K. and Akef, M., 2007. Evaluation of damage to stands (trees and regeneration) resulted from implementing log exploitation method in Shafaroud, Guilan. Iranian Journal of Natural Resources, 60(3): 931-947 (In Persian). - Nikooy, M., 2007. Optimization of production costs and reducing logging damages in Caspian forests. Ph.D. thesis, Faculty of Natural Resources, University of Tehran, Karaj, 214p (In Persian). - Nikooy, M., Naghdi, R. and Ershadifar, M., 2013. Survey of directional felling and analysis of effective factors on felling error (case study; Iranian Caspian forests). Caspian Journal of Environmental Science, 11(2): 177-184. - Nikooy, M., Rashidi, R. and Kocheki, G., 2010. Residual trees injury after selective cutting in broadleaf forest in Shafaroud. Caspian Journal of Environmental Science, 8(2): 173-179. - Pinard, M.A., 1994. The reduced-impact logging project. Tropical Forest Update, 4(3): 11-12. - Roger, J.S. and Sun, C., 1993. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transaction Neural Network, 4: 156–159. - Sarikhani, N., 2001. Forest Utilization. Tehran University Press, Tehran, 776p (In Persian). - Shahin, M.A., Jaksa, M.B. and Maier, H.R., 2008. State of the art of artificial neural networks in geological engineering. Electronic Journal of Geotechnical Engineering Bouquet, 8: 1–26 - Shourmij, Y., 2009. Economical-environmental evaluation of felling component in two logging methods (traditional and planned) (the case study of district 3 .Nav-Asalem). M.Sc. thesis, Faculty of Natural Resources, University of Guilan, 91p (In Persian). - Siadati, M., 1997. Correct Methods of Tree Felling. Exploitation and Business Office of Forests. Published by Forests, Range and Watershed Management Organization, Tehran, 76p (In Persian). - Sobhani, H. and NaeijNouri, A., 2006. Instruction of Determining Accumulation Places and Skidding Trail Networking. Published by Forests, Range and Watershed Management Organization, Tehran, 221p (In Persian). - Stone, M., 1974. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B, 36(2): 111-147. - Sugeno, M. and Kang, G.T., 1988. Structure identification of fuzzy model. Fuzzy Sets Systems, 28:15–33. - Takagi, T. and Sugeno, M., 1983. Derivation of fuzzy control rules from human operator’s control action: 55-60. In: Sanchez, E., (Ed.). Fuzzy Information, Knowledge Representation and Decision Analysis: Proceedings of the IFAC Symposium. Oxford Press, Marseille, France. - Tsukamoto, Y., 1979. An approach to fuzzy reasoning method: 137-149. In: Gupta, M.M., Ragade, R.K. and Yager, R.R., (Eds.). Advances in Fuzzy Set Theory and Applications. Elsevier Press, Amsterdam. - Twomey, J.M. and Smith, A.E., 1997. Artificial neural networks for civil engineers: 44-64. In: Kartam, N., Flood, I. and Garrett, J.H., (Eds.). Validation and Verification. ASCE Press, New York. - Wang J., Charlie L., McNeel J. and Baumgras J., 2004. Productivity and cost of manual felling and cable skidding in central Appalachian hardwood forests. Forest Products Journal, 53: 47-53. - Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8: 338-353. | ||
|
Statistics Article View: 645 PDF Download: 550 |
||
Journal management system. designed by sinaweb