THE ZONATION OF LANDSLIDE OCCURRENCE USING OF SUPPORT VECTOR MACHINES ALGORITHM (CASE STUDY: DARAKEH BASIN)

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.

YAMANI MOJTABA; AHMADABADI A.; ZARE GH.

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

Journal Paper

Paper Information

Journal: GEOGRAPHY AND ENVIRONMENTAL HAZARDS Year:2012 | Volume:1 | Issue:3 Page(s): 125-142

Download Full-Text

Persian Verion

View:

1,157

Download:

439

Cites:

Information Journal Paper

Title

THE ZONATION OF LANDSLIDE OCCURRENCE USING OF SUPPORT VECTOR MACHINES ALGORITHM (CASE STUDY: DARAKEH BASIN)

Author(s)

YAMANI MOJTABA | AHMADABADI A. | ZARE GH. | Issue Writer Certificate

Keywords

LANDSLIDEQ2

ARTIFICIAL INTELLIGENCEQ2

SVM ALGORITHMQ2

ENVIRONMENTAL MANAGEMENTQ2

DARAKEH CATCHMENTQ2

Abstract

Introduction: LANDSLIDE is a geologic process that occurs over a wide variety of spatial and temporal scales in many mountainous landscapes. LANDSLIDEs have a correspondingly wide range of effects that depends strongly on their spatial pattern of occurrence and frequency and magnitude of movement. Mass movements can be the dominant source of erosion responsible for the long-term geomorphic evolution of hillslope morphology. A number of different models have been developed for LANDSLIDE susceptibility mapping, such as heuristic, conditional probabilistic, logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), and deterministic models. These approaches have been reviewed in detail in recent publications (Carrara et al., 1995, 1999; Aleotti and Chowdhury, 1999; Guzzetti et al., 1999; Dai et al., 2002; Guzzetti, 2003; van Westen, 2004; Brenning, 2005; Wang et al., 2005; Chacon et al., 2006; Alexander, 2008; Corominas and Moya, 2008; van Westen et al., 2008). Among these approaches, SVM modeling is becoming increasingly popular. The procedure is based on statistical learning theory, and involves a training phase with associated input and target output values. The trained model is then used to evaluate a separate set of test data (Yao et al., 2008). SVM modeling has been undertaken less frequently than other approaches to LANDSLIDE susceptibility mapping. Yao et al. (2008) showed that two-class SVM modeling produced more accurate susceptibility maps than one-class SVM and LR modeling on the natural slopes of Hong Kong, China. Brenning (2005) showed the predictive power of LR, SVMs and bootstrap-aggregated classification trees in a case study of the Ecuadorian Andes. In that study, LR with stepwise backward-selection of variables yields the lowest error rates and demonstrates the best generalization capabilities. LANDSLIDE is among slope process dominant on South Alborz and especially in Darakeh basin. Identify area that are prone to LANDSLIDE is very important, because near of basin to Tehran city, tourism aspect and human settlement in the basin. In this research we tried using SVM ALGORITHM is determined and zonation LANDSLIDE hazard and Areas susceptible to LANDSLIDEs in the basin. Seems that output of research has a role in sustainable ENVIRONMENTAL MANAGEMENT and a document used in future planning for development of infrastructure…

Cites

No record.

References

No record.

Cite

APA: Copy

YAMANI, MOJTABA, AHMADABADI, A., & ZARE, GH.. (2012). THE ZONATION OF LANDSLIDE OCCURRENCE USING OF SUPPORT VECTOR MACHINES ALGORITHM (CASE STUDY: DARAKEH BASIN). GEOGRAPHY AND ENVIRONMENTAL HAZARDS, 1(3), 125-142. SID. https://sid.ir/paper/226682/en

Vancouver: Copy

YAMANI MOJTABA, AHMADABADI A., ZARE GH.. THE ZONATION OF LANDSLIDE OCCURRENCE USING OF SUPPORT VECTOR MACHINES ALGORITHM (CASE STUDY: DARAKEH BASIN). GEOGRAPHY AND ENVIRONMENTAL HAZARDS[Internet]. 2012;1(3):125-142. Available from: https://sid.ir/paper/226682/en

IEEE: Copy

MOJTABA YAMANI, A. AHMADABADI, and GH. ZARE, “THE ZONATION OF LANDSLIDE OCCURRENCE USING OF SUPPORT VECTOR MACHINES ALGORITHM (CASE STUDY: DARAKEH BASIN),” GEOGRAPHY AND ENVIRONMENTAL HAZARDS, vol. 1, no. 3, pp. 125–142, 2012, [Online]. Available: https://sid.ir/paper/226682/en

Related Journal Papers