Evaluation of Performance of Support Vector Machine Algorithm in Landslide Susceptibility Zoning in Ahar-chai Basin

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KARAMI FARIBA; BAYATI KHATIBI MARYAM; Kheirizadeh Mansour; Mokhtari Asl Aboulfazl

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Journal: GEOGRAPHY AND ENVIRONMENTAL HAZARDS Year:2020 | Volume:8 | Issue:32 Page(s): 1-17

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Title

Evaluation of Performance of Support Vector Machine Algorithm in Landslide Susceptibility Zoning in Ahar-chai Basin

Author(s)

KARAMI FARIBA | BAYATI KHATIBI MARYAM | Kheirizadeh Mansour | Mokhtari Asl Aboulfazl | Issue Writer Certificate

Keywords

LandslideQ1

Susceptibility ZoningQ1

Support Vector Machine (SVM) AlgorithmQ1

Radial Basis FunctionQ1

Ahar-Chai BasinQ1

NW IranQ1

Abstract

Introduction: Landslides are one of the most important geological hazards worldwide (Chen et al., 2018). Despite advances in science and technology, these events continue to result in economic, human, and environmental losses worldwide (Alimohammadlou, Najafi, & Yalcin, 2013). Globally, Landslides cause about 1200 deaths and 3. 5 billion dollars of loss each year (Zhang, Han, Han, Li, Zhang, & Wang, 2019). About 66 million people live in Landslide-prone areas (Chen et al, 2018). Landslide susceptibility (LS) mapping is essential in delineating Landslide prone areas in mountainous regions. Landslide susceptibility is the propensity of soil or rock to produce various types of Landslides (Chalkias Ferentinou, & Polykretis, 2014). From the beginning of the 1970s, the interest of both geoscientists and engineering professionals in LS zonation and the increasing emphasis on the use of Geographic Information Systems (GIS) technology led to the development of many methods such as weights-of-evidence model (Karami, 2012; Wang, Guo, Li, He, & Wu, 2019) logistic regression (Pham, Pradhan, Bui, Prakash, & Dholakia, 2016; Raja, Ç iç ek, Tü rkoğ lu, Aydin, & Kawasaki, 2017), artificial neural networks (Chauhan, Sharma, Arora, Gupta, 2010؛ Tsangaratos & Benardos, 2014), neuro-fuzzy (Aghdam, Varzandeh, & Pradhan, 2016; Lee, Hong, & Jung, 2017; Chen et al., 2019)...

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APA: Copy

KARAMI, FARIBA, BAYATI KHATIBI, MARYAM, Kheirizadeh, Mansour, & Mokhtari Asl, Aboulfazl. (2020). Evaluation of Performance of Support Vector Machine Algorithm in Landslide Susceptibility Zoning in Ahar-chai Basin. GEOGRAPHY AND ENVIRONMENTAL HAZARDS, 8(32 ), 1-17. SID. https://sid.ir/paper/358568/en

Vancouver: Copy

KARAMI FARIBA, BAYATI KHATIBI MARYAM, Kheirizadeh Mansour, Mokhtari Asl Aboulfazl. Evaluation of Performance of Support Vector Machine Algorithm in Landslide Susceptibility Zoning in Ahar-chai Basin. GEOGRAPHY AND ENVIRONMENTAL HAZARDS[Internet]. 2020;8(32 ):1-17. Available from: https://sid.ir/paper/358568/en

IEEE: Copy

FARIBA KARAMI, MARYAM BAYATI KHATIBI, Mansour Kheirizadeh, and Aboulfazl Mokhtari Asl, “Evaluation of Performance of Support Vector Machine Algorithm in Landslide Susceptibility Zoning in Ahar-chai Basin,” GEOGRAPHY AND ENVIRONMENTAL HAZARDS, vol. 8, no. 32 , pp. 1–17, 2020, [Online]. Available: https://sid.ir/paper/358568/en

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