مقاله انگلیسی رایگان در مورد شبکه تحلیلی و فازی بر مبنای سیستم تصمیم گیری – الزویر 2018

Landslides as a type of mass movements involve slow or fast movement of soil and stone materials, or both on the slopes downwards, under the force of gravity (Crosta and Clague, 2009). Landslides are known as one the most common geological disasters which cause damages and casualties worldwide (Bianchini et al., 2016; Bui et al., 2012; IGOS, 2004; Shahabi et al., 2014; Wang et al., 2016). While landslide occurrences include 9% of all natural disasters in the past decade, it is expected that this trend will increase in the coming years, due to the development of urbanization, deforestation and climate change (Yilmaz, 2009; Zare et al., 2013). The damaging effects of landslides include loss of life, rapid soil loss, and degradation of agricultural lands, gardens, roads, and engineering structures (Hassanzadeh Nafuti et al., 2012). Given the extent of the damages mentioned, it was explicitly stated that the cost of studying this phenomenon is much less than the damage. Therefore, to understand the susceptibility of hill slopes, landslide risk zones in different regions are addressed (Shadfar et al., 2007). Landslide susceptibility has been defined as the probability of a landslide occurring in a region based on local terrain conditions (Brabb, 1984; Ciampalini et al., 2016). Zoning and preparation of a landslide susceptibility map is a complex process (Brabb, 1991; Chen et al., 2016) that shows possible and sensitive areas to landslides through some effective factors by generalizing the occurrence of slope failures (Akgun, 2012; Van Westen, 2000). Landslide susceptibility maps provide important and valuable information for predicting landslides hazards which include an indication of the time scale within which particular landslides are likely to occur in the future (Atkinson and Massari, 2011). In light of GIS based landslide risk mapping, the multicriteria decision analysis (MCDA) methods provides a rich collection of procedures and techniques for structuring decision problems and designing, evaluating and prioritizing alternative decisions (Feizizadeh and Blaschke, 2014; Feizizadeh and Kienberger, 2017). MCDA has been widely applied to support environmental planning processes, where MCDA can provide a transparent combination of a problem from different perspectives and a systematic assessment of the alternatives (Huang et al., 2011; Keisler and Linkov, 2014; Kiker et al., 2005; Mustajoki and Marttunen, 2017; Voinov et al., 2016). There has been a vast body of research around the world on evaluation of landslide mapping based on GIS-MCDA methods (Feizizadeh and Blaschke, 2013; Feizizadeh et al., 2014a; Feizizadeh et al., 2014b). The GIS-MCDA methods and models used by researchers to prepare a landslide risk map are the analytical network process (ANP) (Abedi Gheshlaghi and Valizadeh Kamran, 2016; Neaupane et al., 2008; Neaupane and Piantanakulchai, 2006; Roostaei et al., 2015), fuzzy methods (Anbalagan et al., 2015; Bibi et al., 2016; Bui et al., 2015; Pourghasemi et al., 2012; Tangestani, 2009; Vakhshoori and Zare, 2016), neuro-fuzzy hybrid methods (Aghdam et al., 2016; Dehnavi et al., 2015; Pradhan, 2013; Vahidnia et al., 2010), and logistic regression (Ayalew and Yamagishi, 2005; Bui et al., 2016; Demir et al., 2015; Devkota et al., 2013; Sangchini et al., 2016; Umar et al., 2014).