Introduction

Floods are among the natural disasters that, according to the United Nations Development Program's global report on the risk of natural disasters, floods along with earthquakes and droughts have the highest rank in terms of financial and human losses (Beheshti et al., 2018: 22). Inappropriate human interference in ecosystems, uncontrolled exploitation of forests and excessive livestock grazing are among the most important factors that aggravate floods, which reduce the storage capacity of watercourses, and waste fertile soils, and as a result, increase surface runoff and the occurrence of floods. (Chitti, 1382: 39). The increasing trend of floods in the last five decades shows that the number of floods in the 80s has increased almost 10 times compared to the 40s, and in other words, it has increased by 90% (Abdi, 2015: 20). Flooding in urban basins occurs at a high speed on flat and impermeable surfaces that have been created by man-made drainage systems. According to this factor, the urban state of finding natural areas causes an increase in the volume and intensity of runoff and the occurrence of floods in the downstream areas (Broumand Nesab, 2011: 2). One of the basic steps to reduce the harmful effects caused by floods is to know the flood-prone areas and classify these areas in terms of flood risk (Petial, 2008: 8) so that based on the results, it is possible to make decisions regarding the use of land and different land uses, including The future optimal development of cities and villages, service and production agriculture, made a decision. The catchment area of Izeh city due to its location on a mountainous plain is one of the areas at high risk of flooding, which causes great damage to this city and its surrounding areas every year. Flood risk potential and its zoning using multi-criteria models and geographic information system



Materials and methods

The required data were collected in a library manner and the data analysis will be in the scale of the studied area in a comparative manner. The materials used in this research include the criteria of distance from waterway lines, slope, direction of slope, formation. , the shape of the basin, precipitation, land use, which were used to determine the vulnerable area against the risk of flood. Each of these criteria has its own characteristics and applications, which were extracted from the following data: 1- Land use map of the region 2- Topographical maps 1:50000 (Izeh, Kay Maghsoudi, Shahrak Shivand, Bagh Malik). 3- Geological maps (Mount Asmari, Taghdis, Kamestan with a scale of 1:100,000). 4- Map of digital model of elevation lines (DEM). 6- Rainfall data statistics (rain gauge stations: Izeh, Bagh Melek, Sosun, Dehdz, Barangard and Qala Tel, Water and Electricity Organization, 2014). - The map of the distance from the waterway lines. 7- Flood prone and problematic areas were controlled in the field, so that the accuracy of the action in prioritization and the conformity of the zoning results with the reality in the basin will be known more. Fuzzy standardization in Hydrasi software based on the degree of membership defined in the domain of fuzzy number and valued between (0 ≤ ud(x) ≤ 1), then weighted by the Analytical Hierarchy (AHP) method through Expert Choice software and Finally entered the model.



Discussion

In this research, seven effective layers in the occurrence of floods including: rainfall, slope, slope direction, land use, distance from waterways, geological formations and height were prepared and classified using the weighting method of Analytical Hierarchy Method (AHP). Expert Choice software (Table 1) in this method, after forming a pairwise comparison matrix related to each risk, in order to determine the importance of the relative weight, we transferred them to the Expert Choice environment. The most weight is assigned to the layer that plays a greater role in the occurrence of each. In this next step, a 7x7 matrix was created to compare, and different criteria were compared two by two, and the resulting values were assigned based on the hourly sieve.



Based on Thomas L. Sati’s method to calculate the values and the special vector of the columns they are added together and each cell is divided by the sum of the corresponding column and normalized. Then, the average of the normalized table rows is calculated as the final weight. The resulting number for the compatibility index (CR) in the resulting matrix is equal to 0.07, which indicates an acceptable level of the weighting results, which is shown in the form of a diagram (Figure 1). Among the parameters It has been concluded that the most important factor in the flooding of this region is primarily the rainfall. Sudden torrential rains in the winter and spring seasons cause flooding in this region. The presence of steep slopes in the high mountain heights leads the runoff to the plain. On the other hand, this basin is a closed basin, and all the water flows into the basin to two lakes (Miangaran and Bandan). When heavy rains occur, the lakes overflow and the city is threatened by floods from the north and southeast.



Conclusion

The analysis of the Silber risk zoning map according to the WLC model shows that the main areas at risk are in the east and south areas, 79.17 kilometers of the basin are at very high risk of flooding and 58.52 kilometers are at high risk. Also, the results Of . The Vicor model map shows that 91.79 km of the studied basin is at risk of high flood, which mostly includes the eastern and southwestern areas of the basin. One of the main reasons for flooding in these areas is more rainfall in mountainous areas, high slope, impermeable formations; The presence of valleys and the high density of waterways in these areas have caused a large amount of runoff to be directed to the west and central areas of the plain