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Prediction commercial and cordwood volume of broadleaves using Artificial Neural Networks (Case study: Gorazbon distric of Kheyrood forest, Nowshahr) | ||
| تحقیقات جنگل و صنوبر ایران | ||
| Article 16, Volume 25, Issue 3 - Serial Number 69, January 0, Pages 517-527 PDF (800.43 K) | ||
| Document Type: Research article | ||
| DOI: 10.22092/ijfpr.2017.112886 | ||
| Authors | ||
| Fatemeh Gorzin* 1; Manouchehr Namiranian2; Mahmoud Omid3; Mahmoud Bayat4 | ||
| 1M.Sc. Forestry, Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 3Prof., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran | ||
| 4Assistant 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 | ||
| Artificial Neural Networks; Kheyrood forest; Multi Layer Perceptron | ||
| References | ||
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