Landslide modelling and susceptibility mapping using AHP and fuzzy approaches

Landslide which is defined as the movement of a mass of rock, debris or earth down a slope,1 is known as one of the most destructive natural hazards all over the world2 but fortunately, it can be prevented through different available methods including prediction and wise constructions. Damages caused by landslides can be ranging from top soil loss to human death, they also can change the figure of the landscape such as causing landslide dams that occur when a huge landslide blocks a river flow and causes a lake,3 this type of dam frequently occurs in tectonically active mountains which cause sudden landslides.4 In case of losing top soil, the problem doubles when the removed soil is carried into the reservoir of a dam which can occupy a big proportion of its capacity. Landslides can have an impact on forests in a way that tree biomass would recover between 80-200 years5 that means the damaged area will be bare for a long time. Different landslide classifications have been released in case of landslides, however, the most reliable and famous classification is done by Warnes6 in which landslides are primarily classified by the type of movement. The cohesion and friction forces of rocks and soils prevent the slopes from moving down due to gravity, however, sometimes because of a variety of causes, the adjustment of the equilibrium fails and it results in a landslide.7 Landslides might be either natural or human induced8 also depending on the geographical and climatic conditions, both kinds of triggers can cooperate and cause a landslide. There are some major factors such as unfavorable geology, rise in underground water and heavy rainfall during few days before the movement,9 there are also a vast number of factors which with a relatively difference in importance, can cause a landslide these factors can be listed as constructions, slope angle, slope aspect, curvature of the slope, distance to fault, climate condition10,11 land cover12 etc. Depending on the region, the importance of each factor might be lower or higher than others. Many studies have been applied to know the landslides better also to understand how to minimize the direct and indirect costs and prevent them. Landslide susceptibility maps are helpful tools for decision makers and future constructors for spatial plannings,13 therefore many different approaches have been applied to make the most optimum prediction of landslide disasters, some examples of these approaches are Index of Entropy (IOE)14 Weigh of Evidence15 Statistical Index method,16 Logistic Regression4,16,17 Analytic Hierarchy Process technique16,18,19 Fuzzy Logic.20–23 Barrile et al.24 used GIS-based fuzzy logic in order for road network planning.24 Feizizadeh et al.23 applied fuzzy and AHP approaches for landslide susceptibility mapping and the output was considered as satisfactory due to 53% overlap with observed landslide layer.25 Bui et al. used the GIS-based fuzzy neural network for landslide susceptibility mapping and through testing multiple membership functions figured out that Gaussian membership function was the most suitable function Kayastha et al.26 applied AHP for landslide susceptibility mapping and according to the reliable outcome, they considered AHP as a reasonable approach Althuwaynee et al.27 compared AHP Logistic regression, and Bayesian methods in landslide susceptibility mapping, and found AHP as the most reliable method for criteria rating. Wang et al.28 applied three methods (multi-criteria statistical analysis, logistic regression, and multivariate adaptive regression spline models) and the results showed that the last method which had 77% overlap with observed landslides map, had the most productivity among other methods.28 Poiraud used 5 methods (Indicator, weightof-evidence, logistic regression, decision tree, one condition unit) to obtain landslide susceptibility map and since all outcomes were reliable, he used the combination of all results as the final outcome that highly matched with observed landslide layer.29 Ciurleo et al.30 used statistical methods for landslide susceptibility mapping in small and big scales and found out that although all the methods show reliable results, a bigger scale mapping is more reasonable because more criteria can be considered.30 In order to obtain landslide susceptibility map, Erener et al.31 tested logistic regression, GIS-based multi criteria analysis, and Association Rule Mining and through the validations realized that logistic regression and ARM method had better outcome.31 Keh-Jian Shou considered climate as a dynamic