Prediction commercial and cordwood volume of broadleaves using Artificial Neural Networks (Case study: Gorazbon distric of Kheyrood forest, Nowshahr)

Document Type : Research article

Authors

1 M.Sc. Forestry, Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

3 Prof., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

4 Assistant Prof., Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

Abstract

Decision-making in natural resources often leads to complexities beyond the statistical empirical methods,therefore we need new solutions than algorithmic methods. Artificial neural networks (ANN) technology mimics the human brain in the process of problem solving.The aim ofthis studywas to predict the commercial volume and cordwood volume using this technique (Artificial Neural Network). For this purpose, 367 marked trees in the experimental and educational forest of Kheyrood were selected. Some factors including diameter at breast height, diameter at stump, stump height, total height, topographic factors (slope, aspect and elevation), species, tree situation and minimum median diameter of last log were measured. The factors were considered as input network. Multi-layer Perceptron network (MLP) was used for modeling. The result showed that Multi-layer Perceptron network (with the 0/94 and 0/71 R2, and 0/233 RMSE) has acceptable accuracy to predict the commercial and cordwood volume.

Keywords


- Bayat, M., 2014. Growth and yield models for uneven-aged and mixed broadleaf forest by neural network method (Case study: Gorazbon district in Kheyroud forest, North of Iran). Ph.D. thesis, Faculty of Natural Resources, University of Tehran, Karaj, 147p (In Persian).
- Bayat, M., Namiranian, M., Omid, M., Rashidi, A. and Babayi, S., 2016. Applicability of artificial neural network for estimating the forest growing stock. Iranian Journal of Forest and Poplar Research, 24(2): 214-226 (In Persian).
- Bayati, H. and Najafi, A., 2011. Application of artificial intelligence in trees stems volume estimation. Journal of Renewable Natural Resources Research, 2(2): 52-59 (In Persian).
- Bayati, H. and Najafi, A., 2013. Comparison between artificial neural network and regression analysis in trees stem volume estimation. Journal of Forest and Wood Product, 66(2): 177-191 (In Persian).
- Coulson, R.N., Folse, J.L. and Loh, D.K., 1987. Artificial intelligence and natural resource management. Science, 237: 262-267.
- Etemad, V., 2002. Study of quantitative and qualitative characteristics of beech tree seed in Mazandaran province. Ph.D. thesis, Faculty of Natural Resources, University of Tehran, Tehran, 258p (In Persian).
- Ghanbari, F., Shataee, Sh., Dehghani, A.A. and Ayoubi, Sh., 2009. Tree density estimation of forests by terrain analysis and artificial neural network. Journal of Wood and Forest Science and Technology, 16(4): 25-42 (In Persian).
- Gimblett, R.H. and Ball, G.L., 1995. Neural network architectures for monitoring and simulating changes in forest resources management. AI Applications, 9(2): 103-12.
- Gorzin, F., 2015. Prediction volume of trees by artificial neural networks (Case study: Kheyroud Forest). M.Sc. thesis, Faculty of Natural Resources, University of Tehran, Karaj, 78p (In Persian).
- Jafari, M., Vafakhah, M. and Abghari, H., 2012. Performance comparison of two activation functions namely sigmoid and hyperbolic tangent in artificial neural networks for storm runoff coefficient forecasting (Case study: Barariyeh Watershed, Neishabour). Journal of Water and Soil Conservation, 20(2): 85-103 (In Persian).
- Kia, M., 2010. Neural Network in Matlab. Kian Rayaneh Sabz Press, Tehran, 323p (In Persian).
- Namiranian, M., 2010. Tree Measurement and Forest Bio-inventory. University of Tehran Press, Tehran,   574p (In Persian).
- Ozçelik, R., Diamantopoulou, J.M., Brooks, J.R. and Wiant, H.V., 2010. Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of Environmental Management, 91(3): 742-753.
- Peng, C. and Wen, X., 1999. Recent applications of artificial neural networks in forest resource management: an overview. AAAI Technical Report WS-99-07, Published by American Association for Artificial Intelligence (AAAI), USA, 8p.
- Safi Samgh Abadi, A., 2003. Forest multi-objective planning by artificial neural networks. Ph.D. thesis, Faculty of Natural Resources, University of Tarbiat Modares, Noor, 156p (In Persian).
- Shamekhi, T., 2011. Regulation and Administration of Natural Resources (Forests and Rangelands). University of Tehran Press, Tehran, 476p (In Persian).
- Soltani, S., Sardari, S., Sheykhpour, M. and Mousavi, S., 2010. Understanding the Principles and Applications of Artificial Neural Networks. Published by Scientific and Cultural Organization of Nas, Tehran, 216p (In Persian).
- Yolme, Gh., Moayyeri, M.H. and Mohammadi, J., 2013. An introduction to different methods for renewal volume of felled trees. Journal of Conservation and Utilization of Natural Resources, 1(1): 99-111 (In Persian).
- Zobeiri, M., 2005. Forest Inventory (Tree Measurement). University of Tehran Press, Tehran, 401p (In Persian).
- Zobeiri, M., 2007. Forest Biometry. University of Tehran Press, Tehran, 407p (In Persian).