Forest road planning using artificial neural network and GIS

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.

JAVANMARD M.; ABDI E.; GHATEE M.; MAJNUNIAN B.

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

Journal Paper

Paper Information

Journal: Iranian journal of Forest Year:2018 | Volume:10 | Issue:2 Page(s): 139-152

Download Full-Text

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

Persian Verion

View:

633

Download:

Cites:

Information Journal Paper

Title

Forest road planning using artificial neural network and GIS

Author(s)

JAVANMARD M. | ABDI E. | GHATEE M. | MAJNUNIAN B. | Issue Writer Certificate

Keywords

Artificial neural networkQ2

Forest road networkQ1

GISQ1

Linear regressionQ1

Abstract

Forest roads are constructed to facilitate forest protection, reforestation, logging operations and maximizing the value of forest products. Therefore forest roads are key infrastructures in the development of the region. This study aims to plan Forest road network using Artificial neural network and GIS regarding forest road technical principles. First the criteria were chosen using Delphi method and then they were weighted regarding their importance in road planning. After that the criteria were combined with corresponding weighs to achieve suitability map based on the degree of suitability for road allocation. Value and coordinates of each pixel were extracted by ENVI software and were normalized in the range of 0-1 for modeling by MATLAB software. In this study two neural networks were used for modeling, including multilayer perceptron (MLP) and radial-bases functions (RBF). The neural networks estimated suitability of different pixels in other districts based on the Patom district results. Using an ArcView GIS extension, PEGGER, two Forest road networks were planned. The results showed that MLP provides better ability for estimating suitability of pixels for road passage in comparison with RBF with the R2 of 0. 994. A Linear regression was also used to compare the results of the proposed neural networks. The results revealed that neural network improves the results in comparison with the Linear regression and results showed that the second road alternative was optimum network with regard to the unit cost.

Cites

No record.

References

No record.

Cite

APA: Copy

JAVANMARD, M., ABDI, E., GHATEE, M., & MAJNUNIAN, B.. (2018). Forest road planning using artificial neural network and GIS. IRANIAN JOURNAL OF FOREST, 10(2 ), 139-152. SID. https://sid.ir/paper/123054/en

Vancouver: Copy

JAVANMARD M., ABDI E., GHATEE M., MAJNUNIAN B.. Forest road planning using artificial neural network and GIS. IRANIAN JOURNAL OF FOREST[Internet]. 2018;10(2 ):139-152. Available from: https://sid.ir/paper/123054/en

IEEE: Copy

M. JAVANMARD, E. ABDI, M. GHATEE, and B. MAJNUNIAN, “Forest road planning using artificial neural network and GIS,” IRANIAN JOURNAL OF FOREST, vol. 10, no. 2 , pp. 139–152, 2018, [Online]. Available: https://sid.ir/paper/123054/en

Related Journal Papers