Landslide Hazard Mapping Using the Artificial Neural Network a Part of Haraz Watershed

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.

SEPAHVAND A.R.; MORADI H.R.; ABDOLMALEKI P.

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

Journal Paper

Paper Information

Journal: Watershed Management Research Year:2017 | Volume:29 | Issue:113 Page(s): 9-19

Download Full-Text

Persian Verion

View:

661

Download:

Cites:

Information Journal Paper

Title

Landslide Hazard Mapping Using the Artificial Neural Network a Part of Haraz Watershed

Author(s)

SEPAHVAND A.R. | MORADI H.R. | ABDOLMALEKI P. | Issue Writer Certificate

Keywords

LandslideQ2

Artificial Neural NetworkQ2

Back Propagation AlgorithmQ1

Sigmoid FunctionQ1

Abstract

A large part of Iran› s formed mountainous areas, so each year, Landslides cause damage to structures, residential areas and forests, creating sedimentation, mud floods and finally cause filling reservoirs. Since forecasting of the Landslide occurrence is out of human knowledge in both temporally and spatially, so the Landslide susceptibility mapping is considered in order to be shown how much a mountain slope is susceptibility to a mass movement. The purpose of this study was to preparation of Landslide susceptibility map in a Part of Haraz Watershed. In this study nine factors including slope percent and aspect, geology, precipitation, distance from the road and the river and faults, land use and elevation were used. These factors were then used with an Artificial Neural Network to analyze Landslide susceptibility. Each factor’ s weight was determined by the back-propagation training method. Landslide locations were identified in the study area from interpretation of aerial photographs and field survey data. From seventy eight points of Landslide occurrence and seventy eight points of nonLandslide occurrence determined in this area, 70 and 30 percent used for training and testing, respectively. The results of the Artificial Neural Network showed that 1-14-9 structure whit 0. 2 learning rate is optimal structure and Root Mean Square Error is 0. 051. Accuracy of network in training and testing phase was equal 92. 307 and Coefficient of Determination was equal 0. 962.

Cites

No record.

References

No record.

Cite

APA: Copy

SEPAHVAND, A.R., MORADI, H.R., & ABDOLMALEKI, P.. (2017). Landslide Hazard Mapping Using the Artificial Neural Network a Part of Haraz Watershed. WATERSHED MANAGEMENT RESEARCHES (PAJOUHESH-VA-SAZANDEGI), 29(113 ), 9-19. SID. https://sid.ir/paper/200698/en

Vancouver: Copy

SEPAHVAND A.R., MORADI H.R., ABDOLMALEKI P.. Landslide Hazard Mapping Using the Artificial Neural Network a Part of Haraz Watershed. WATERSHED MANAGEMENT RESEARCHES (PAJOUHESH-VA-SAZANDEGI)[Internet]. 2017;29(113 ):9-19. Available from: https://sid.ir/paper/200698/en

IEEE: Copy

A.R. SEPAHVAND, H.R. MORADI, and P. ABDOLMALEKI, “Landslide Hazard Mapping Using the Artificial Neural Network a Part of Haraz Watershed,” WATERSHED MANAGEMENT RESEARCHES (PAJOUHESH-VA-SAZANDEGI), vol. 29, no. 113 , pp. 9–19, 2017, [Online]. Available: https://sid.ir/paper/200698/en

Related Journal Papers