fuzzy logic model

In this research, to analyze the karst geomorphology of Dimeh and Pirghar karst masses in Chaharmahal and Bakhtiari province located in the Zagros highlands, karst dolin were first modeled with the closed contour line method (CCLS) in karst masses. Then the modeling of karstification zones has been done using two approaches, fuzzy logic and multivariate linear regression. The validation results of the calculated figures related to the area under the receiver operating characteristic (ROC) showed that the fuzzy logic model based on the area under the curve (AUC) of both karst massifs has a higher degree of efficiency compared to the multivariate linear regression model. 

The increasing population pressure, drastic land use changes, and rapid urbanization have made cities susceptible to various natural and human-induced hazards. Therefore, enhancing the resilience of urban settlements is crucial, particularly in the context of megacities. This research investigated urban resilience levels, prioritized resilience indicators, and compared resilience status between Districts 5 and 6 of Tabriz Metropolitan Area. The study employed a descriptive-analytical method with a developmental-applicative focus. Data were collected through library research, field surveys, and questionnaires, targeting the residents of Districts 5 and 6. A sample size of 342 individuals was determined using Structural Equation Modeling (SEM) and the gamma exponential method. In this study, 37 observed indicators of urban resilience were analyzed across 4 latent categories: social, economic, institutional-management, and physical-infrastructure. Latent indicators were prioritized based on their significance and impact through multiple regression analysis using SPSS software, while observed indicators were assessed via confirmatory factor analysis in LISREL software. A fuzzy logic model was employed within a GIS environment to map the effective indicators. Results revealed that the social latent index ranked highest in District 5 of Tabriz Metropolitan Area with a beta coefficient of 0.384 (33.83%) followed by the physical-infrastructural (0.305, 26.87%), economic (0.267, 23.52%), and institutional-management (0.179, 15.77%) indicators. In District 6, the physical-infrastructural latent index led with a beta coefficient of 0.528 (42.58%) followed by the institutional-managerial (0.337, 27.18%), social (0.265, 21.37%), and economic (0.110, 8.87%) indicators. Additionally, the findings indicated that the northwestern and central parts of District 5 exhibited better resilience, ranking in the upper-middle tier, while the southern areas showed poor resilience. Conversely, the northern parts of District 6 also displayed weak resilience, ranking in the lower average range, while the southern and central regions were categorized as upper average in terms of resilience.

Awareness of the amount of the danger of ablation, the possibility of recognizing critical areas and arranging of managing plans and protecting plans achieved. the present study is produced aiming the use of fuzzy logic EPM and BLM in predicting the power of ablation and calculation of the whole illuviation that has been produced in a year in the Razan basin by help of evaluation and GIS then the correctness of the studies have been compared according to the results of BLM model the more of the studied are wan in low erosion in the rest of the paper by using EPM model the whole of illuviation that had been produced in Razan basin was calculated according to the results of EPM model the power of ablation in the studied area laid in the ablation of middle class also the results of game operator of fuzzy %98 of study area was show reel in five class of the less clanger to the most danger final the results of credit evaluating of models showed that the results of EPM model had more similarity with the prepare data of the study that show the correctness of EPM model in analyzing ablation of the studied area according to the ranking of the area and using EPM model 28/44- 32/16 and 39 /40 % Percent of the space of the area laid in the classes of the more less .less – middle.

 Landslide is a term that encompasses a variety of amplitude motions and causes the movement of a mass of material in the slopes. And creep is classified. Natural slope instability is one of the geomorphological and geological phenomena that plays an effective role in deforming the earth's surface. Identifying areas with potential for landslides and their zoning is one of the key steps in managing environmental hazards and reducing the damage caused by this phenomenon, because this phenomenon causes financial and human costs, soil and land degradation and increased sediment production at the basin outlet. It becomes. Iran with its predominantly mountainous topography, high tectonic activity and seismicity, diverse geological and climatic conditions, has the most natural conditions to create a wide range of landslides. The purpose of this study is to zoning the risk of landslides on the Sarab-Nir road. In this research, two models of network analysis and fuzzy logic are examined and evaluated. It is hoped that eventually, by preparing a landslide risk zoning map, it will be of great help to planners and managers in order to reduce potential damages and find safer locations for development, construction and road construction.

Sarab-Nir road is located between East Azarbaijan province and Ardabil province and is a communication route between these two provinces, whose geographical coordinates are 37 degrees and 94 minutes to 38 degrees and 03 minutes north latitude and 47 degrees and 53 minutes to 48 degrees and 01 minutes. It is east longitude. In this study, network analysis model was used to determine areas prone to fall and zoning. In order to better understand the causes of landslides and also to organize the research in the field, the study area was visited and 15 geographical points from different areas of the study area were recorded. The geographical location of the points prone to fall was also recorded with GPS. Then, according to the network analysis model, information layers were prepared in ArcGIS software. The information layers for landslide risk zoning are: fault, slope, slope direction, distance from road, and distance from waterway, land use, geology (lithology), precipitation, altitude and vegetation. The elevation file or digital model of the elevation of the area was prepared with an accuracy of 30 meters from the USGS site and the desired DEM is a digital file obtained from the AST‌ER sensor and according to this DEM, the information layer such as streams, slope and direction ‌The slope was obtained.

Four maps have been developed to investigate landslide hazards, which are rainfall, slope, elevation and land use layers. After creating information layers in order to prepare the final landslide hazard map, fuzzy information layer maps were created. In this study, in order to determine the effect of different classes of criteria on landslide sensitivity zoning, the layers are based on the type of performance of each in the landslide event using fuzzy membership functions in the range of zero to 1 fuzzy. Were made. The results obtained from the information layers and finally the landslide hazard map show that altitudes of more than 2000 meters have the highest share of landslides, and altitudes of 1400 meters have been significant landslides due to the instability of the slopes against Climatic and environmental factors. Also, most of the landslides occurred at a distance of 3 to 6 km from the faults, which shows the importance of faults against landslides. About 40% of landslides occur in very high-risk classes. This indicates that the model has a high capability in predicting landslides. It is necessary to explain that most of the landslides occur in the area of Saein pass, which have very favourable conditions for the occurrence of range movements that start from 25 km of mirage and continue for a distance of 15 km of Nir.

Factors such as slope, precipitation and geology play a more important role in landslides than other factors. Slopes of 60 to 80% have the greatest impact on landslides, which are more pronounced at altitudes above 2000 meters. Therefore, altitudes above 2000 meters have the most landslides. Also, due to the direct relationship between altitude and climatic fluctuations in these altitudes, the amount of precipitation is higher and, of course, has a great impact on the occurrence of landslides. In these areas, vegetation is at a minimum and due to the cold region, the vegetation in these areas is very small, which prepares the conditions for landslides and due to the presence of sedimentary formations such as sandstone, Siltstone mudstone with tuff interbreeds in the area, the conditions for landslides have become more prone and because these formations lose their stability sooner and are strongly influenced by physicochemical factors, they are more prone to landslides than other formations. To be. According to the results, the low risk floor with the highest value, 405.44 square kilometers, occupies approximately 30.87 percent of the area, but the very high risk floor with 288.2 square kilometers and the high risk class with 23.23 square kilometers. , Occupy a total of 37.25% of the area of risk classes.

The main purpose of the present study is to assess the relationship between environmental vulnerability and infrastructure resilience of rural communities (in Bojnourd County), so that one can improve the resilience capacity in rural areas. The research was conducted in a descriptive-analytical method and in nature it is applicable in local planning and rural development systems. Research variables have been selected after reviewing theoretical foundations and literature on vulnerability and resilience assessment in Iran and other countries. Accordingly, two general indicators of vulnerability assessment and infrastructure resilience of rural communities were developed using 26 criteria in FAHP and fuzzy logic (Gamma operator with the thresholds of 0.7, 0.8 and 0.9) in the GIS environment. The results indicate that approximately 56.69% of the County’s area has a high and very high environmental vulnerability, and 47 villages were situated in this area. The survey of the resilience index indicates that there is a significant and direct relationship between low resilience in rural areas and environmental vulnerabilities in the eastern, southern and northeastern parts of Bojnourd County. In addition, based on the FAHP output, about 31 percent of the rural areas of the county, and based on the output of fuzzy logic, about 53 percent of the rural areas of the county are in a very high resilience level). The analysis of the output results of FAHP and fuzzy logic in GIS with a Kappa coefficient of 0.166 showed that the results of the two models have comparatively weak but direct correlation, which was determined by measuring the accuracy of the models using ground control points. Fuzzy logic model with a Kappa coefficient of 0.772 is more accurate than the FAHP model with a Kappa coefficient of 0.550.

  Landslide is considered as one of the natural hazards ever occurring throughout the world and is of great importance. This phenomenon is one of the major geomorphic processes affecting the evolutionary landscape in mountainous regions, which has caused catastrophic accidents. Due to the special climate conditions, physiography and change of the country, Iran has always faced with the problem of mass movements, and it is necessary to pay attention to this natural limitation. Lorestan province also has diverse geological features such as petrography, land management, seismicity and special climate conditions, including areas with slip potential. The topographic and geological conditions of the study area are such that the slip of rock and soil fragments from small to large scale has been provided. Fuzzy logic is a logic of several values, that is, its parameters and variables, in addition to the number of 0 or 1, can take all the values between these two numbers. The basis of the difference between fuzzy methods and other methods is to define the membership function. The membership function can be used to determine the degree of attribution of the elements of the reference set to its subset. The operator of the fuzzy society is the collection community.In this way, it extracts the maximum membership membership. The fuzzy subscription operator is the collection subscription. In that way, it extracts the minimum degree of membership. Fuzzy algebraic multiplication multiplies all the information layers together. Because of the nature of the numbers between zero and one, which is the same as the membership membership in a fuzzy set, the operator makes the number of the numbers smaller and goes down to zero. The complementary fuzzy algebra operator is obtained by the algebraic multiplication. Therefore, in the outbound map, unlike the fuzzy algebraic operator, the value of pixels goes toward one. The fuzzy gamma operator is the product of multiplication, fuzzy coherent multiplication in the fuzzy algebraic summation. The results obtained from this operator are more accurate than other operators.An artificial neural network is a computational mechanism that can provide a series of new information by gathering information and calculating them. In the artificial neural network, the structure of the human brain and the body's neural network is similar to that of a brain that has the power to learn, make and decide. In the neural network model for preparing the network from the layers, along with a number of real samples, they entered the network as inputs, and with this method a pattern was obtained between the input parameters and the areas where the landslide was located. A probability ratio was used to determine the land slide sensitivity index. In order to facilitate artificial neural network convergence, the values ​​of the input neurons were normalized. To estimate the accuracy of artificial neural network, the mean squared error error was used. An artificial neural network with multilayer perceptron structure with error propagation algorithm and non-linear sigmoid function as an activation function was used. The simple learning factor was ignored because of convergence and failure to make a valid error. Also, the error rate of the variable learning coefficient was higher than the Levenberg-Marquard method, which is why the Levenberg-Marquard method was used. For training and network testing, 80% of the data was used for training and 20% for testing. The final structure of the 1-14-8 grid was considered appropriate and based on this structure, the final zoning was performed.The operators of the fuzzy logic model were used. The result of the fuzzy community operator generated the maximum membership membership membership. The fuzzy share operator extracts the minimum membership membership. The result of the operator of the fuzzy algebraic multiplication is reduced to zero numbers. The output map of the operator of the fuzzy algebra sum of the value of the pixels is close to the maximum. In order to modulate the very high sensitivity of the fuzzy algebraic operator and the very low accuracy of the fuzzy algebraic operator, a 0.9% gamma-gamma operator was used. Kappa coefficient for artificial neural network model was 0.83 and for fuzzy logic model 0.66. The evaluation of the results obtained from the fuzzy logic model and the artificial neural network using kappa statistical coefficient shows that the artificial neural network with Kappa statistical coefficient is 0.91 compared to the fuzzy logic model with a kappa coefficient of 0.88 more than the prediction of the risk of landslide In the Seymareh Chenar Basin. Based on the zoning, the artificial neural network model was 10.12, 22.92, 31.44, 20.76, 15.16 percent of the area in the low, medium, high and very high risk classes has it.

     Soil erosion is a process in which soil is separated from its original bed and transported to another location with the help of a moving agent [17]. Soil erosion is one of the serious problems in the watersheds of Iran and can be considered as one of the most important barriers to sustainable agriculture and natural resources development [6]. Soil erosion results in the destruction of agricultural land, the reduction of soil fertility, environmental negative impacts, and a very important threat to the sustainable production of agricultural products [8]. Erosion, the result or results of the interaction of a set of natural and human factors, which, in accordance with the specific circumstances of the region, are one or more factors acting as the main and determining factors [7]. In general, erosion is a natural phenomenon that human activities can exacerbate. Historical evidence suggests that erosion and destruction of soil have resulted in the collapse and destruction of human civilizations [9] .Awareness of the extent of erosion in catchment areas, the possibility of comparing them and identifying critical basins and implementing watersheds. The amount of erosion and destruction of the land requires the zoning of erosion of the land for the preparation and collection of information [8]. Destruction of vegetation (especially the plant species of moss, which is the dominant species of the region) and the cutting of trees and shrubs (oak and orange trees, which form the largest area of the forests of the area) cause soil to be deflated against erosion and cause debris to be gutted and even swept away.

     In this research geological maps, topographic maps, Landsat 8 satellite images, climatic data of the study area in research, including temperature and precipitation, and the 30-meter-high elevation digital model of the study area were used to prepare the slope map, the slope direction and the extraction of the waterways. ArcGIS software was used to create databases, spatial and spatial analysis, and finally to implement the model and use the ENVI software to process satellite imagery. Fuzzy logic is a logic of several values, that is, its parameters and variables, in addition to the number of 0 or 1, can take all the values between these two numbers. The basis of the differences between fuzzy methods and other methods is to define the membership function. The membership function can be used to determine the degree of attribution of the elements of the reference set to its subset. The operator of the fuzzy society is the collection community. In this way, it extracts the maximum membership. The fuzzy subscription operator is the collection subscription. In that way, it extracts the minimum degree of membership. Fuzzy algebraic multiplication multiplies all the information layers together. Because of the nature of the numbers between zero and one, which is the same as the membership in a fuzzy set, the operator makes the number of the numbers smaller and goes down to zero. The complementary fuzzy algebra operator is obtained by the algebraic multiplication. Therefore, in the outbound map, unlike the fuzzy algebraic operator, the value of pixels goes toward one. The fuzzy gamma operator is the product of multiplication, fuzzy coherent multiplication in the fuzzy algebraic summation. The results obtained from this operator are more accurate than other operators. To evaluate the accuracy of the model, Kappa statistical coefficient was used.

     The results show that the kappa statistical coefficient shows that the 0.97 fuzzy gamma operator with 0.83 kappa coefficient has excellent accuracy in soil erosion zonation in Seymareh Chinar basin. Investigating the results of this study shows that areas with a steep slope, poor vegetation, bay lands, presence of limestone and dolomitic stones have much more erosion. This type of erosion is located in the northwest, south and southwest of the studied basin. In the south and southwest of the region, due to the high slope, low soil depth, excessive grazing of livestock, degradation of vegetation and forest by the inhabitants of the region and plowing the land towards the slope, led to greater erosion. In the west, south and center of the area due to uneven topography and in some areas, soil depth, cereal, steppe, in some cases, blueberries and slopes less than other parts of the area, erosion is less. According to the results of this research, as well as field observations and lack of attention of the people of the area to soil degradation and destruction of the dominant vegetation of the region, it seems that the study area in research needs urgent planning to prevent soil erosion. This suggests that in this basin, the separation and displacement of soil particles is so limited that implementation of the necessary water and land conservation programs and the use of land has a lot of limitations.  Discussion & Conclusions:      To do this study, using a fuzzy logic model, the database consists of slope, directional, petrographic, land use, lagoon, rainfall, soil and channel formation, and then the required data were obtained by processing the data. The operators of the fuzzy logic model were used. The result of the fuzzy community operator generated the maximum membership. The fuzzy share operator extracted the minimum membership. The result of the operator of the fuzzy algebraic multiplication is reduced to zero numbers. The output map of the operator of the fuzzy algebra sum of the value of the pixels is close to the maximum. In order to modulate the very high sensitivity of the fuzzy algebraic operator and the very low accuracy of the fuzzy algebraic operator, a 0.9% gamma-gamma operator was used. Based on the zoning done using fuzzy function, 16.19, 31.37, 27.77, 17.98, and 34.66 percent of the area of the area are located in very low, moderate, high and very erosion classes. Kappa coefficient was 0.83 for fuzzy logic model. Eliminating the dominant vegetation of the region, especially the mossy vegetation, which has roots and prevents soil erosion, cuts off oak trees to plow in the direction of slope and changes the unauthorized use, all have added to the severity of erosion in this area.