Document Type : Research Article
Authors
1
Associate Professor, Department of Geography, Shahid Bahonar University of Kerman, Kerman,Iran
2
Associate Professor of Gemorphology, Shahid Bahonar University, Kerman, Iran,
3
MSc. of Spatial Planning, Department of Geography, Shahid Bahonar University of Kerman, Iran
10.22067/geoeh.2025.88912.1502
Abstract
Introduction
Natural hazards are unfortunate accidents that cause great and irreparable damage to the natural environment and man-made phenomena every year. In the meantime, floods are one of the most important recurring natural threats that threaten the human yard and the economy of communities. Factors influencing the increase of floods in recent years include the rapid expansion of urbanization, land use change, land conversion and destruction of vegetation and soil, encroachment and housing in the river area, climate change and the intensity of short-term rainfall. Zarand city has been prone to floods due to some special climatic and geographical conditions. Therefore, due to the need to assess flooding in Zarand plain as a predisposed area, and considering that no studies in this field have been conducted in the region so far, In this research, using Sentinel 1 multispectral image processing and Fuzzy-AHP method, which is one of the multi-criteria decision analysis techniques, in GIS environment, a map of different factors affecting flooding has been prepared. , Weight and combine to identify areas prone to flooding. The main purpose of this study is to identify potential flood risk areas in Zarand city, Kerman province using multi-criteria decision analysis technique and Sentinel 1 radar images.
Method
Floods in the region depend on various hydrological and geomorphological factors. In this study, eight influential factors including cumulative flow criteria, discharge capacity, height, distance from waterway, land cover, runoff coefficient, slope and lithology were used. After preparation, these layers are weighted by fuzzy hierarchical method. The Fuzzy-AHP method was first proposed by Chang (1996: 649). The main difference between this method and AHP method is the difference in the method of weighting criteria and options, so that in this method weighting is done fuzzily. And the binary comparison matrix is expressed in fuzzy. The pairwise comparison matrix is formed in pairs with the help of decision makers' opinions about the importance of factors in relation to each other. The elements of this matrix are triangular fuzzy numbers, the first component of which is the minimum number of comments, the second component of which is the average of the polls, and the third component of which is the maximum amount of the polls. In this regard, 20 experts were surveyed. Based on expert opinions, the criteria are compared in pairs and based on the fuzzy numbers in Table 1, the final weight of each criterion is calculated.
Results and Discussion
In order to identify and prepare a flood risk map, effective factors and criteria in flooding were studied and finally mapped in GIS environment. Map 8 Effective criteria for flooding can be seen in Figure 3. In order to achieve the final map of groundwater resources potential in the fuzzy hierarchical method, each criterion must first be weighted and combined accordingly. The criteria for binary comparison and weighting are expert opinions. Table 2 shows the binary comparison matrix of the criteria. Since each of the mentioned criteria also has sub-criteria within it, which also have different effects on flooding, each of them should be compared and weighed in a binary way. In this regard, a binary comparison matrix was prepared separately for all criteria (Tables 3 to 10). Fuzzy maps prepared from each of the criteria can also be seen in Figure 4. Among the various criteria, the cumulative flow criterion is considered as the most important factor, which is drawn in Figure a3. As can be seen in this map, the study area is divided into 5 floors in terms of cumulative flow. The higher the cumulative flow rate, the higher the water flow accumulation. As a result, in the binary comparison and weighting stage, the highest weight is assigned to the last floor and then to the other floors. In the binary comparison matrix, the cumulative flow criterion of the last floor sub-criterion of weight is 0.258 and the first-class sub-criterion of weight is 0.126 (Table 3). Other influential factors of flooding are classified and weighted in the same way, which can be seen in Tables 5 to 10. Table 11 shows the weight of the criteria, sub-criteria and the final weight of each. In this table, the final weight of each sub-criterion is obtained by multiplying the weight of the main criterion by the weight of the sub-criterion. In order to prepare the flood risk index and map, all the prepared and weighted maps were combined by fuzzy hierarchical method in GIS environment. And using the quantitative classification method, the map was classified. Figure 5 shows the flood risk map. As can be seen in this figure, in terms of flood risk, the study area is divided into 5 categories of very low, low, medium, high and very high risk. About 5% of the study area is very high risk (18800 hectares), 23% high risk (94100 hectares), 44% medium risk (179700 hectares), 22% low risk (88200 hectares) and 6% very low risk (23100 Hectares), forms. High and very high danger zones are mostly located in the plains and alluvial areas.
Conclusions
By combining the effective layers in creating floods, the study area was potentialized in terms of flood risk. The results show that about 5% of the study area is very high risk (18800 hectares), 23% high risk (94100 hectares), 44% medium risk (179700 hectares), 22% low risk (88200 hectares) And constitutes a very small 6% (23,100 hectares). High and very high danger zones are mostly located in the plains and alluvial areas. The results show that in addition to agricultural land, many residential areas, especially in rural areas, are at risk of flooding. Threshold method on Sentinel 1 radar images showed that these images have a good ability to detect floods. Because radar waves can penetrate the cloud, they can be used to monitor floods in rainy and cloudy weather. While optical satellite images do not have this capability. Also, the results of validation of Fuzzy-AHP method, while confirming the use of this method in determining flood risk, confirm the efficiency of this method, as a relatively accurate method in decision making.
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