mokaram

Introduction Using information about landslide occurrence can get accurate information about landslide hazard assessment (Dai et al., 2002). Dangerous effectives of landslides is in relation to the economic system of many countries (Nefeslioglu et al., 2008). There are different methods for landslide susceptibility mapping such as Malczewski 1996; Jankowski et al. 1997; Nyerges et al. 1997; Bennett et al. 1999; Feick and Hall 1999; Jankowski and Nyerges 2001a, b; Kyem 2004. Thus, the region was selected fuzzy-AHP method to investigate landslide susceptibility in east of Kerman province, Iran.
TPI: Andrew Weiss presented a very interesting and useful poster at the 2001 ESRI International User Conference describing the concept of Topographic Position Index (TPI) and how it could be calculated (Weiss 2001). Using this TPI at different scales, plus slope, users can classify the landscape into both slope position (i.e. ridge top, valley bottom, mid-slope, etc.) and landform category (i.e. steep narrow canyons, gentle valleys, plains, open slopes, mesas, etc.). The algorithms are clever and fairly simple. The TPI is the basis of the classification system and is simply the difference between a cell elevation value and the average elevation of the neighborhood around that cell. Positive values mean the cell is higher than its surroundings while negative values mean it is lower. The degree to which it is higher or lower, plus the slope of the cell, can be used to classify the cell into slope position. If it is significantly higher than the surrounding neighborhood, then it is likely to be at or near the top of a hill or ridge. Significantly low values suggest the cell is at or near the bottom of a valley. TPI values near zero could mean either a flat area or a mid-slope area, so the cell slope can be used to distinguish the two. TPI is naturally very scale-dependent. The same point at the crest of a mountain range might be considered a ridgetop to a highway construction crew or a flat plain to a mouse. The classifications produced by this extension depend entirely on the scale you use to analyze the landscape. TPI (Eq. (1)) compares the elevation of each cell in a DEM to the mean elevation of a specified neighborhood around that cell. Mean elevation is subtracted from the elevation value at center. (1)
where M0 = elevation of the model point under evaluation, Mn = elevation of grid, n = the total number of surrounding points employed in the evaluation.
Fuzzy-AHP Fuzzy logic was initially developed by Lotfi Zadeh (1965) as a generalization of classic logic. Lotfi Zadeh (1965) defined a fuzzy set by memberships function from properties of objects. A membership function assigns to each object a grade ranging between 0 and 1. The value 0 means that x is not a member of the fuzzy set, while the value 1 means that x is a full member of the fuzzy set. Traditionally, thematic maps represent discrete attributes based on Boolean memberships, such as polygons, lines and points. Mathematically, a fuzzy set can be defined as following (Mc Bratney et al., 2000): (2)
In order to define the fuzzy rules and fuzzy-AHP models, the critical level of each parameter for corn production was extracted using some references in the study area.
AHP is a structured technique for organizing and analysing complex decisions. This method is based on a pair-wise comparison matrix. The matrix is called consistent if the transitivity Equation (5) and reciprocity (Equation (6) rules are respected.
aij = aik · akj (3)
a ij= 1/ a ji (4)
where i, j and k are any alternatives of the matrix.
Results and discussion The aim of this study was to determination of landslide susceptibility in the east of Kerman, Iran. Nine major properties were selected to landslide susceptibility including slope, aspect, elevation, distance from fault, land use, distance from road, geology, rainfall and distance from stream were evaluated. Then, raster map was prepared in ArcGIS for each of the parameters. Also, the fuzzy and AHP method used for predictive landslide susceptibility map. The results of the fuzzy and AHP method in this study show that the west of the study area was suitable for landslide. The relationship between landslide susceptibility and landform showed that the possibility of landslide on the peaks, ridges and hills is high.
Conclusion Based on the different conditions of the study area, such as the financial condition of the people and government, age distribution of the population, etc., the landslide susceptibility map with the fuzzy-AHP method can be used.

The main purpose of this study is to prepare soil fertility maps for Wheat using Fuzzy classification in Zarghan area، Fars province. In the present study، seven soil parameters، such as Copper (Cu)، Iron (Fe)، Manganese (Mn)، Zinc (Zn)، Organic mater (OC)، Potassium (K)، Phosphor (P) are chosen for fertility analysis and thematic maps are developed for each of the parameters with IDW model. Different Fuzzy membership functions obtained from the literature were employed and the weights for each parameter were calculated according to an Analytic Hierarchy Process (AHP) that relies on pair wise comparisons. The results with the Fuzzy theory showed 17% of the study area as highly fertility for wheat 40 % as moderately fertility، 13% as weak fertility. For determine accuracy soil fertility map used kappa coefficient. Based on the results it has been concluded that Fuzzy method allows obtaining results that seems to be corresponded with the current conditions in the area.