In this section, the two methods are discussed separately.
4.1. AHP method:
This method in 6 stages of studies includes determining the effective factor, selecting criteria, Accreditation, data processing, fuzzy and overlap.
4.1.1. Determining effective factors
At this stage, different layers of information and sources were examined and according to the factors affecting the occurrence of floods (mentioned by various researchers), and based on the characteristics of Khuzestan province, the following factors were selected as effective factors.
- Land slope: includes the information layer of the topographic slope of the area.
- Fault density and fractures: This layer includes all faults in the region.
- Geology: This layer of lithology includes all formations in the region.
- Waterway density: This layer includes the main and secondary waterways of Khuzestan province.
4.1.2. Selection of criteria
In selecting the effective criteria in evaluating the general rule, it is assumed that these criteria should be determined in relation to the problem situation,Munier and Honoria, .[26] 2021
The process of generating benchmark maps is based on GIS functions, which include entering geographic data (acquisition, reformatting, land referencing, collecting and documenting relevant data), data storage (non-spatial data), It is the processing and analysis of data (to obtain information) and the output of data,Carter et al, 2021 [13]. In order to compare different measurement scales (based on different indicators), scaling or standardization should be used, which is measured through the elements of dimensionless converted indicators,Fullér et al, 2019 [16].Based on the type of information available to create maps, benchmark maps can be classified into definite, probabilistic, and fuzzy states,Hsu et al, 2021 [20]. In these studies, the fuzzy method has been used.
4.1.3. Accreditation
The importance of benchmark maps in achieving output is not the same. Therefore, it is necessary to score benchmark maps, or in other words, to be validated,Wang, 2016[42]. The validity of each criterion indicates its importance and value compared to other criteria in location or routing operations,Tang and Hsu, 2018 [38].
Accreditation is based on expert knowledge and based on the opinion of experts, taking into account various factors such as study area, location or routing parameters, the impact of each parameter, etc. Shen et al, 2019 [35]. Accreditation is done in several different ways, in which the Analytic Hierarchy Process (AHP) model is used.
In fact, the process of hierarchical analysis is one of the decision-making systems for multiple criteria that is based on expert knowledge, Yamagishi andBhandary, 2017 [44]. In hierarchical analysis, it is possible to formulate the problem and consider different quantitative and qualitative criteria,Abdel Rahman et al, 2021 [1].After forming the pairwise comparison matrix and completing it with the mentioned methods, the weight of each criterion is determined. To do this, use the program written in the MATLAB software environment and Expert Choice software, and by entering the data of the pairwise comparison table, which are fuzzy, the validity of each criterion is determined (Tables 1 and 2).
Table 1
Pair comparison and validation of main criteria
Main criteria
|
lithology
|
Distance from waterway
|
Distance from the fault
|
Slope
|
AHP
|
lithology
|
1
|
2
|
4
|
4
|
0.5
|
Distancefrom
waterway
|
0.5
|
1
|
2
|
2
|
0.24
|
Distance from the fault
|
0.25
|
0.5
|
1
|
1
|
0.13
|
Slope
|
0.25
|
0.5
|
1
|
1
|
0.13
|
Table 2
Paired comparison and validation of lithology sub-criteria
Sub-criteria of lithology
|
Evaporate
|
Carbonate
|
Marl
|
classic
|
AHP
|
Evaporate
|
1
|
9
|
10
|
10
|
0.75
|
Carbonate
|
0.11
|
1
|
2
|
2
|
0.12
|
Marl
|
0.10
|
0.50
|
1
|
1.00
|
0.07
|
classic
|
0.10
|
0.50
|
1.00
|
1
|
0.7
|
4.1.4. Data processing
After determining the effective factors in the occurrence of subsidence, the relevant information layers should be prepared. The following section describes how to prepare the data.
4.1.4.1. Slope layer processing
Basin slope is one of the most important factors that control the surface flow time and water concentration in the river,Ali et al, 2019 [7].The slope of a basin affects the amount of infiltration, surface flow rate, soil moisture and the amount of groundwater in the basin,Nayak,2021 [27]. Basically, steeply sloping basins often have flood flows Abuzaid and Fadl, 2018 [4].In areas with high slopes, the velocity of water flow increases and as a result, with the dissolution of surface layers, especially in evaporative layers, the probability of subsidence increases Abuzaid et al, 2020 [3].
In mountainous areas, increasing the current intensity along with permeability can be considered as an effective factor in creating subsidence Abdel Rahman et al, 2018. [2] In these studies, after preparing the digital elevation model (DEM), the slope map of Khuzestan province was prepared using the Slope function (Figs. 3 and 4).
4.1.4.2. Waterway compaction layer processing
The network of basin waterways shows how runoff is discharged from the basin surface,Saidi et al, 2021 [32]. Therefore, the more the waterways of a basin evolve and develop, the more subsidence of that basin indicates,Meng et al, 2017 [25].
To prepare this layer, first a digital file (Shapfile) of waterways in Khuzestan province was prepared and combined with the waterways from the DEM layer in ArcGIS program.
The output layer in Google Earth software was then validated with real streams, and new streams were added and some removed (Fig. 5). After calculating the length of the final lines, the density layer of the streams was calculated using the Density function in the ArcGIS environment (Fig. 6).
4.1.4.3. Lithology layer processing
The most important factor in causing subsidence is the resistance of the constituents of the basin surface to erosion,Lange et al, 2019 [22].The strength of formations depends on the type, type and composition of the constituent minerals, the way of crystallization, the amount of pores, stratification, the way of erosion, interlayer, etc. Semeraro et al, 2016 [33].To prepare this layer and classify the different lithologies of the basin in terms of subsidence potential, the geological map of Khuzestan province prepared by the Geological Survey of Iran (center of Ahvaz) has been used (Fig. 7).In this layer, due to the different importance of rock layers in the rate of subsidence, first, according to the type of formations, 4 types of lithology with evaporitic names (gypsum and anhydrite), detrital (conglomerate, sandstone, silt and clay), carbonate (dolomite) And lime) and marl were divided.In the next step, with the help of the Reclassify function, the maximum validity and weight are given to the evaporating layer, then to the carbonate layers, and finally to the marls and detritus.
4.1.4.4. Fault density layer processing
Another factor in creating subsidence is the distance of layers from faults in the region and the impact of these faults on them Gasperini et al, 2021. [17]By applying stress to different rock units, the tectonic agent causes faults and fractures in them, and these fractures create secondary porosity in the rock, which increases the permeability and ultimately increases the risk of subsidence. Hoc Yakupoğlu et al, 2019[43]. The higher the density of these fractures, the greater the permeability, Semeraro et al, 2016 [33].Given the size of the study area, it is virtually impossible to locate and measure fractures. However, since the presence of fractures depends on the fault, on a large scale, by locating the faults and calculating their density, the highest fracture density can be logically achieved. To prepare this layer, the faults drawn in the geological maps of Khuzestan province were prepared with the faults drawn using integration satellite images and the distribution map of the faults of the province (Fig. 8). Finally, using the fault layer and applying the Density function in ArcGIS environment, a fault density map of Khuzestan province was prepared (Fig. 9).
4.1.5. Layer standardization or fuzzy
In the field of GIS, to determine subsidence potential, standardize GIS-based raster criteria by assigning a value between [1 − 0] to each pixel using fuzzy membership functions such as Sigmoidal, J shape, Linear, or many other forms. The complexity is non-uniform,Carter et al, 2021[13].In this study, standardization of data according to their quantitative and qualitative nature was done in two ways. Quantitative data were standardized using fuzzy membership functions and qualitative data were standardized using the rasterization method and assigning a value between [0–1].
4.1.5.1. Standardization of quantitative layers
As mentioned in the section above, quantitative layer standardization was performed using fuzzy membership functions. Quantitative layers include slope, fault density, and waterway density (Figs. 10 and 11).
4.1.5.2. Standardization of quality layers
Standardization of qualitative layers (geological layer) has been done using rasteration. In this method, values between zero and one [1 − 0] were given to the geological map and finally converted to raster using these values.
4.1.6. Overlap
Overlap is a spatial function that can combine layers of spatial data obtained from separate sources for location applications using hybrid models. The new layer (output) is a function of two or more input layers.
Due to the fact that effective criteria and sub-criteria have different validities and all of them must participate in the overlap, the index overlap method has been used for this purpose. In this method, the standardized layer obtained from each criterion (x_i) is multiplied by its validity (its weight) by the criterion (w_j). This is done for all criteria and sub-criteria and new layers are obtained that are overlapped using various functions such as AND, OR, SUM, Product and GAMA.
After overlapping the main criteria with different functions, finally the output of a function is considered as the best option, in accordance with field and statistical evidence, for zoning of flood potential. At this stage, the output of AHP method is considered as the final output. Is(Fig. 12).
4.2. Fuzzy inference system programming in MATLAB environment:
The fuzzy inference system is an intelligent system whose task is to learn expertise from experts and to be able to make decisions based on the expertise it has learned Munier and Hontoria, 2021 [26]. In this system, the aim is to define a system that determines the risk level of the project based on different information layers,Singh and Lone, 2021[34].
In these studies, based on the value and validity of each information layer, a specific weight for that layer in MATLAB program is introduced and finally, based on the defined weight, the level of risk in the study area is determined, the most important of which include the following:
4.2.1. Introduction and initial validation of layers:
Lithology layer: As mentioned before, this layer is the most important layer affecting the results, so with high accuracy, the whole of Khuzestan province was divided into four types of evaporitic lithology, carbonate, marl and shale and finally detrital, and the effect of each lithology with a code 1 to 4 (lithology, respectively) were introduced.
Layers of distance from the fault and distance from the waterway: The closer the distance from the fault and the distance from the waterway, the greater the risk, so from two scales of distance less than 300 meters (high risk) and more than 300 meters (low risk) for risk It has been used in these studies.
Slope layer: The degree of risk based on the slope in these studies were divided into three categories: 0 to 20 degrees (low risk), 20 to 70 degrees (steep and high risk), 70 to 90 degrees (low slope and low risk).
4.2.2. Fuzzy input variables (fasification):
The attribution function is used to fuzzy the 4 input layers because it becomes a fuzzy function when its attribution function is known and its linguistic variable is knownHsu et al, 2021[20].Therefore, at this stage, after the initial segmentation and validation of the layers, with the help of the fuzzy part of the MATLAB program, it has been used to define the different rules of these layers.In these studies, Mamdani FIS Type is used, which is the most widely used fuzzy inference system used in public works.
Mamdani fuzzy inference was first introduced as a method to create a control system by synthesizing a set of linguistic control rules obtained from experienced human operators [1]. In a Mamdani system, the output of each rule is a fuzzy set,Mamdani and Assilian, 1975[15].Since Mamdani systems have more intuitive and easier to understand rule bases, they are well-suited to expert system applications where the rules are created from human expert knowledge, such as Geological diagnostics Darbor et al, 2020.[14]The inputs along with the output were divided into 5 sections using a triangular function (Fig. 13).
The intensity of changes in these intervals is indicated by the letters ( VL, L, M, H, VH)- (Fig. 14). For lithology, 4 types of change intensities including detrital (V-LOW), marl (LOW), carbonate (MED) and evaporative (High) were used. For the distance from the fault and the distance from the waterway, high change intensity (0 to 300 m) and low change intensity (300 to 1000 m) were used .For slope, the intensity of change was very low (0 to 20 degrees), high (20 to 70) and low (70 to 90).
4.2.3. Definition of rules:
Duction of correct and practical rules plays a very important role in the final decision of the care system, Harahap et al, 2021 [19]. These rules must be made very carefully and with the help of the opinions of experienced experts, Yanar et al, 2020 [45]. In these studies, 49 laws were introduced for this system according to the opinions of different experts (Fig. 15).
4.2.4. Import inference system and inputs in MATLAB application using Evalfis function:
With the help of this function, general inputs can be introduced to the fuzzy system and the output of all of them can be received, Akgun et al, 2021[5].In fact evalfis (fis,input) evaluates the fuzzy inference system fis for the input values in input and returns the resulting output values in output,Yamagishiand Bhandary, 2017[44].
According to Fig. 16, the lithology entrance is marked with an L, the distance from the waterway is marked with an R, the distance from the fault is marked with an F, and the slope is entered with a mark D. As can be seen, by entering the exact values of the study area, the degree of risk of the study area in field operations can be determined. According to Fig. 16, if the type of Avari lithology (1) and the distance from the river is 600 meters and the slope of the layers is 80 degrees and the distance from the fault is 400 meters, the output is 0.6, which according to the definitions for this number system This is the study section (Fig. 16).Also, according to Fig. 17, if the lithology of the area is carbonate (number 3) and the distance from the river is 100 meters and the distance from the fault is 80 meters and the slope is 60 degrees, the risk level of this area is 2.5, i.e., medium to weak risk. The results seem quite reasonable considering that the amount of hazards in carbonate layers is more than detrital layers. It should be noted that the numerical scales of lithology include 1: detrital, 2: marl and shale, 3: carbonate and 4: evaporitic, respectively.This method will be very useful in preparing geological maps with scales of 1: 5000 to 1: 25000 and also in preparing maps of high-risk geological areas that are prepared with the aim of carefully studying a small part of the earth.
4.3. Validation of results with field and statistical evidence
In order to interpret, analyze and validate the prepared zoning, 2000 points from different regions of the province that have statistics and evidence of subsidence were evaluated on the output of the prepared subsidence map.
The results of this study show that the outputs of this research are more than 90% close to reality. These studies have been carried out in different cities of Khuzestan province that have low to high subsidence potentials. In the lower part of Gotvand city in the north of Khuzestan province is presented as an example of field studies and telemetry.
Gotvand city is located in the southwest of Iran and in the north of Khuzestan province and on the edge of Khuzestan plain. In the northwest of this city, there are heights of Bakhtiari Formations (with conglomerate lithology), Aghajari (with sandstone lithology), Gachsaran (with evaporitic lithology) and Mishan (with Marne lithology). They show a very high agreement with the results of the study of the two proposed models (Fig. 18). Field studies conducted in this area showed that in some parts of the Gachsaran Formation, which are close to seasonal and permanent waterways, several sinkholes are being formed (Fig. 19).
In this region, with the proximity to the Avari and Marni formations (Aghajari and Mishan), the amount of subsidence has been reduced so that in these formations, despite the proximity to the waterway and different slopes, no subsidence effects are observed (Fig. 20).
As stated, the results of both models presented to investigate the potential for subsidence in this area show a very good agreement with field observations so that in parts of the Gachsaran Formation that are less distant than waterways and faults According to these models, they are less dangerous than the areas that are closer to these complications.
According to the models introduced for this region, Bakhtiari, Aghajari and Mishan formations are divided as very low risk areas and Gachsaran formation is divided into two high-risk and very high-risk areas.