PREDICTION COMMERCIAL AND CORDWOOD VOLUME OF BROADLEAVES USING ARTIFICIAL NEURAL NETWORKS (CASE STUDY: GORAZBON DISTRIC OF KHEYROOD FOREST, NOWSHAHR)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

GORZIN F.; NAMIRANIAN M.; OMID M.; BAYAT M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Forest and Poplar Research Year:2017 | Volume:25 | Issue:3 (69) Page(s): 517-527

Download Full-Text

Persian Verion

View:

1,291

Download:

Cites:

Information Journal Paper

Title

PREDICTION COMMERCIAL AND CORDWOOD VOLUME OF BROADLEAVES USING ARTIFICIAL NEURAL NETWORKS (CASE STUDY: GORAZBON DISTRIC OF KHEYROOD FOREST, NOWSHAHR)

Author(s)

GORZIN F. | NAMIRANIAN M. | OMID M. | BAYAT M. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKSQ1

KHEYROOD FORESTQ3

MULTI LAYER PERCEPTRONQ1

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 of this study was 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.

Cites

No record.

References

Cite

APA: Copy

GORZIN, F., NAMIRANIAN, M., OMID, M., & BAYAT, M.. (2017). PREDICTION COMMERCIAL AND CORDWOOD VOLUME OF BROADLEAVES USING ARTIFICIAL NEURAL NETWORKS (CASE STUDY: GORAZBON DISTRIC OF KHEYROOD FOREST, NOWSHAHR). IRANIAN JOURNAL OF FOREST AND POPLAR RESEARCH, 25(3 (69)), 517-527. SID. https://sid.ir/paper/111231/en

Vancouver: Copy

GORZIN F., NAMIRANIAN M., OMID M., BAYAT M.. PREDICTION COMMERCIAL AND CORDWOOD VOLUME OF BROADLEAVES USING ARTIFICIAL NEURAL NETWORKS (CASE STUDY: GORAZBON DISTRIC OF KHEYROOD FOREST, NOWSHAHR). IRANIAN JOURNAL OF FOREST AND POPLAR RESEARCH[Internet]. 2017;25(3 (69)):517-527. Available from: https://sid.ir/paper/111231/en

IEEE: Copy

F. GORZIN, M. NAMIRANIAN, M. OMID, and M. BAYAT, “PREDICTION COMMERCIAL AND CORDWOOD VOLUME OF BROADLEAVES USING ARTIFICIAL NEURAL NETWORKS (CASE STUDY: GORAZBON DISTRIC OF KHEYROOD FOREST, NOWSHAHR),” IRANIAN JOURNAL OF FOREST AND POPLAR RESEARCH, vol. 25, no. 3 (69), pp. 517–527, 2017, [Online]. Available: https://sid.ir/paper/111231/en

Related Journal Papers