COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK (ANN) AND REGRESSION ANALYSIS IN TREE FELLING TIME ESTIMATION

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.

BAYATI H.; NAJAFI A.; ABDOLMALEKI P.

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:2013 | Volume:20 | Issue:4 (50) Page(s): 595-607

Download Full-Text

Persian Verion

View:

896

Download:

Cites:

Information Journal Paper

Title

COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK (ANN) AND REGRESSION ANALYSIS IN TREE FELLING TIME ESTIMATION

Author(s)

BAYATI H. | NAJAFI A. | ABDOLMALEKI P. | Issue Writer Certificate

Keywords

FOREST ENGINEERINGQ2

TIME STUDYQ2

RADIAL BASIS FUNCTIONQ2

MULTI-LAYER PERCEPTRONQ1

FOREST HARVESTINGQ2

Abstract

Tree felling is a most important one among the tree harvesting components. Production estimation of forest equipments is an important part of cost management in forestry operational units which is associated with reduction of the operating expenses. In other words, the high cost of investment in forest utilization, is a good reason for FOREST ENGINEERING research and modeling time. Many techniques such as regression, fuzzy logic, neural networks, etc. are utilized to estimate trees felling time. They make a logical connection between the tree felling time and the independent variables and could be used to predict the tree felling time for the future operations. In this study the regression analysis, two neural network models, MULTI-LAYER PERCEPTRON (MLP) and RADIAL BASIS FUNCTION (RBF) were used to predict the trees felling time in the cutting operations of the Neka Choob Co. In order to collect the felling time data, the time continuous study method was applied. For this purpose, 84 trees were selected from the marked stands and the net felling time was estimated, using the MULTI-LAYER PERCEPTRON and RADIAL BASIS FUNCTION and also by the common method of linear regression analysis. The results showed that the RADIAL BASIS FUNCTION network provided more accurate results in estimating the net tree felling time than the MLP neural network. Comparing the evaluation criteria of ANN with the stepwise regression methods, showed that MLP and RBF neural networks had RMSE value of 0.94 and 0.81, respectively whereas the RMSE value of the regression model was 1.15.

Cites

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

References

No record.

Cite

APA: Copy

BAYATI, H., NAJAFI, A., & ABDOLMALEKI, P.. (2013). COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK (ANN) AND REGRESSION ANALYSIS IN TREE FELLING TIME ESTIMATION. IRANIAN JOURNAL OF FOREST AND POPLAR RESEARCH, 20(4 (50)), 595-607. SID. https://sid.ir/paper/111215/en

Vancouver: Copy

BAYATI H., NAJAFI A., ABDOLMALEKI P.. COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK (ANN) AND REGRESSION ANALYSIS IN TREE FELLING TIME ESTIMATION. IRANIAN JOURNAL OF FOREST AND POPLAR RESEARCH[Internet]. 2013;20(4 (50)):595-607. Available from: https://sid.ir/paper/111215/en

IEEE: Copy

H. BAYATI, A. NAJAFI, and P. ABDOLMALEKI, “COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK (ANN) AND REGRESSION ANALYSIS IN TREE FELLING TIME ESTIMATION,” IRANIAN JOURNAL OF FOREST AND POPLAR RESEARCH, vol. 20, no. 4 (50), pp. 595–607, 2013, [Online]. Available: https://sid.ir/paper/111215/en

Related Journal Papers