fuzzy logic

One of the strategies for water storage in arid and semi-arid areas such as Iran is the construction of underground dams; however, identifying suitable sites for the construction of these structures remains a major challenge. The aim of this paper is to identify the optimal locations for the construction of underground dams using a geographic information system and multi-criteria decision making techniques. Firstly, thematic maps were prepared in ArcGIS environment including geological condition, land use, landslide potential, topography, water stress, aquifer area and distance from dam, village, qanat, stream, fault, airport and road. Hierarchical analysis and fuzzy logic methods were used to weight the prepared maps. By removing unsuitable and restricted areas for the construction of underground dams, a final potential map for this structure was prepared. The results show that the provinces of Razavi and South Khorasan, Kerman, Yazd, Sistan and Baluchistan, Hamedan, Qazvin, Zanjan, Markazi and Qom have greater potential for the construction of underground dams than other provinces in the country. Finally, to validate the results, a comparison was made between the results presented in this study and several successful underground dam projects in the country, showing a relatively close match between the potential map and the implemented structures.

Qualitative assessment of coastal waters affected by seawater salinity can be done using the parameter of chloride in groundwater. This research proposes a supervised artificial intelligence committee machine (SAICM) method for accurate prediction of chloride concentration in groundwater of Sari plain. SAICM predicts chloride concentration as the output of the model by non-linear combination of artificial intelligence models. In this research, Principal Component Analysis (PCA) method was used to identify effective hydrochemical parameters related to chloride concentration as input components to artificial intelligence models. Based on the results of PCA, parameters (Na, K, EC, TDS, SAR) were selected as input components of artificial intelligence models. Firstly, four artificial intelligence models, Sogno fuzzy logic, Mamdani fuzzy logic, Larsen fuzzy logic and artificial neural network were designed to predict chloride concentration. Based on the modelling results, all the models showed a good fit with the chloride data in Sari Plain. Then, the combined SAICM model was built, which combines the prediction results of 4 separate AI models using the nonlinear ANN combiner and determines the chloride concentration more accurately. The results show that the proposed SAICM can estimate chloride concentration with much higher accuracy than individual methods.

Recently, due to the trend of decreasing rainfall and increasing groundwater pumping rate, there have been concerns about the risks caused by the decrease in the volume of aquifer reserves and the drop in the groundwater level, and as a consequence the land subsidence. Also, in Ajabshir plain due to a gradual decrease in the water level, it is necessary to estimate the subsidence and investigate the subsidence potential to prevent its harmful risks in the future. For this purpose, using the ALPRIFT framework, which includes seven layers of parameters affecting subsidence, the subsidence potential map was zoned. The subsidence potential index was obtained in low and moderate ranges. In the next step, using Sentinel-1 satellite images, the subsidence during the years 2020-2021 was estimated to be 2.4 cm, which had a significant correlation with the groundwater level of the water year 2020-2021 and subsidence potential. In addition, artificial intelligence optimization methods including fuzzy logic (Sugeno) and genetic algorithm were used in order to fix the defects of applying expert opinions and increase the correlation between subsidence (Insar) and ALPRIFT, among these models, Sugeno's fuzzy method provided the best correlation between the two subsidence maps and ALPRIFT. The correlation between subsidence with ALPRIFT, ALPRIFT-GA and ALPRIFT-SFL was obtained as 0.46, 0.62 and 0.72 respectively.

Sarbisheh area is located in the west of Sarbisheh and southeast of Birjand, South Khorasan province. This area is located in the Birjand ophiolite melange zone and is a part of the northern part of the Iranshahr-Birjand metallogenic belt. The lithological units in this area include ophiolite melange, flysch facies sediments, pyroclastic rocks and Quaternary sediments. Geochemical studies of stream sediments and identification of geochemical indicators of mineral resources in the region were performed using the results of geochemical analysis and principal component analysis. Remote sensing studies were performed on the ASTER and Landsat satellite images using color composite, selective principal component analysis (crusta) on the Landsat 8 satellite imagery to identify the alteration zones. The lineaments of the region were drawn using the high-pass filter method of the ASTER satellite image and the Google image. Finally, by creating layers of geological units, geochemical data, alteration and lineament and integrating them with fuzzy method, areas with potential mineralization of nickel, chromium, cobalt, copper, lead, zinc and magnesite were identified.

This research is performed to investigate earthquake hazard using Geographical Information System (GIS) and Fuzzy Logic method. First, the necessary factors like active faults; historical, pre-instrumental and instrumental earthquakes; focal mechanism of instrumental earthquakes; geomorphology (elevation and dip), toughness of sediments and active tectonic and also their sub-criteria, which impress earthquake hazard are investigated and changed as information layer in GIS. For the first time, preference of sub- criteria like magnitude and depth of the earthquakes have been regarded. Fuzzy Logic (weighting layers and preferencing them using ArcGIS and Expert Choice software) is applied in earthquake hazard investigation. Finally, in earthquake hazard map of Kerman province, achieved information layers in Arc GIS were merged and various areas (earthquake hazard point of view) were classified to high risk (Kuhbanan, Lakarkuh, Golbaf, Bam, Bardsir and Rigan), moderate risk (Shar-e Babak, Sirjan and Rafsanjan) and low risk (west of the province, Lut and Jazmurian) areas.

Identifying landslide-prone areas provides a basis for slope-stabilization and mitigation programs. In Hablehroud watershed, artificial neural network and fuzzy logic (FL) as one of the methods of multicriteria-decision analysis based on ArcGIS were used in the scientific evaluation of landslide-prone areas. For this purpose, MATLAB, IDRISI and ArcGIS software were used. After preparing landslide-susceptibility maps, the prone zones predicted by FL and multilayer perceptron artificial neural network (MLP-ANN) were compared with the Hablehroud landslide database (distribution map). The results indicate a good overlap between the prone zones predicted by the MLP-ANN and landslide field observations. Finally, the performance of different methods in generating landslide-susceptibility maps were compared to each other using the validation indicators of "quality-sum index (Qs)" and "receiver-operating-characteristic curve (ROC)" to specify the optimal and applicable method for the landslide risk management of the Hablehroud watershed. By analyzing the obtained zoning maps and considering the Qs and "area-under curve (AUC)" values of different FL operators and MLP-ANN for the landslide-susceptibility maps, it is observed that the Qs (1.6299) and AUC (0.806–very good) values of the MLP-ANN are higher than those calculated for the sensitivity maps by different FL operators.

Land subsidence is a nonlinear and complex process that data-driven computational intelligence models can model it. In this study, the accuracy and efficiency of hybrid fuzzy logic gene expression planning hybrid model in estimating land subsidence risk and its factors in Varamin aquifer standardized. For this purpose, after selecting and gathering information from 15 factors affecting the subsidence event based on research records in the GIS environment, they were first standardized by fuzzy membership functions and then gene expression programming method was used to integrate the layers. Finally, using seven important statistical benchmarks based on radar image data, the model was validated in 4 different scenarios in input data and operators. The results showed scenario 1 with input parameters of bedrock level, Debi of pumping wells, groundwater drawdown, geology and operators, +, - ×, ÷, sqr, exp, Ln, ^ 2, ^ 3,3Rt, sin, cos, Atan, is the best model in training and testing. Accordingly, the groundwater drawdown parameter had the highest effect on land subsidence in the study area.

Assessment of Groundwater Vulnerability of Golpayegan Plain Using DRASTIC Method OptimizationQualitative and potential of contamination evaluation of groundwater resources is necessary for contamination protection and their effective management. The Golpayegan study area as one of the most important plains of Isfahan province and one of the most important agricultural areas, has been exposed to nitrate contamination. Therefore, investigation of vulnerable areas of the aquifer in this area with proper method has a more importance. In this study, at first, DRASTIC method was applided to determining the vulnerable areas of Golpayegan plain. Then, to optimizing the DRASTIC method using genetic algorithm, the objective function was defined to the genetic algorithm and by maximizing the objective function, the optimum weights of the DRASTIC parameters were obtained. Also Sugeno fuzzy logic method was used to improve the weights of the DRASTIC method. Nitrate concentration data and its correlation index with vulnerability were used to evaluate the validity of the proposed methods. Correlation indices showed that the optimized DRASTIC using the genetic algorithm has a higher correlations with nitrates and presented the better results than the genera DRASTIC for the region. Optimized map using Genetic Algorithm method showed respectively 6, 23, 34, 21 and 16% of the plains are located in areas with very low, low, moderate, high and very high vulnerability.Correlation indices showed that the optimized DRASTIC using the genetic algorithm has a higher correlations with nitrates and presented the better results than the genera DRASTIC for the region. Optimized map using Genetic Algorithm method showed respectively 6, 23, 34, 21 and 16% of the plains are located in areas with very low, low, moderate, high and very high vulnerability.Keywords: Aquifer, Contamination, DRASTIC, Fuzzy Logic. Golpayegan Plain.

Groundwater resources have long been used by humans as a reliable alternative due to their higher reliability and lower fluctuations, and have declined quantitatively and qualitatively in recent decades due to over-harvesting. In this study, the potential of groundwater resources in Urmia Lake basin was studied using Analytical Hierarchy Process (AHP) method and hybrid model of fuzzy logic- Analytical Hierarchy Process. It is necessary to reduce the negative interaction between the lake and the surrounding groundwater resources as much as possible by managing the abstraction of groundwater and identifying the sensitive areas of the aquifers of this basin and identifying the harvestable areas. First, the effective layers in groundwater potential (layers of height, slope model, land use, river distance, river density, geology, precipitation, evaporation and groundwater level) were prepared by ArcGIS. The results of study showed that in the AHP and the combined model of fuzzy logic, about 18.9% and 25.33% of the region's surface have high potential and are suitable for drilling wells, respectively. Finally, the ROC curve was used to determine the accuracy of the final maps. The accuracy of the final maps prepared by the AHP method and fuzzy logic-AHP was 0.775 and 0.812, respectively, and Fuzzy logic-AHP method had better performance in groundwater potential finding than Analytical Hierarchy Process.

Landslide is known as one of major natural hazards. Landslide susceptibility mapping is known as efficient approach to mitigate the future hazard and reduce the impact of landslide hazards. The main objective of this research is to apply GIS spatial decision making systems for landslide hazard mapping in the 5th segment of Ardebil-Mianeh railroad. Evaluation of the landslide criteria mapping and their relevancy for landslide hazard can be also considered. To achieve the research objectives, an integrated approach of Fuzzy-Analytic Hierarchy Process (AHP), Fooler Hierarchical Triangle and Fuzzy logic methods were employed in GIS Environment.

Within this research, we also aimed to apply GIS spatial decision making systems and in particular GIS multi criteria decision analysis which are available in Arc GIS and Idrisi softwares. We have identified 8 casual factors (including: density of vegetation, land use, faults desistance, distance from rivers, distance from roads, slope, aspect, geology) based on literature review. Accordingly, these layers were prepared in GIS dataset by means of applying all GIS ready, editing and topology steps. The criterion weighting was established based F-AHP approach. The criteria weights was derived and rank of each criterion was obtained. Accordingly, the landslide susceptible zones were identified using GIS-MCDA approaches.

Finally the functionality of each method was validated against known landslide locations. This step was applied to identify most efficient method for landslide mapping. According to the results and based on the values derived from Qs, P, and AUC, the accuracy of fuzzy method was accordingly about 0.33, 0.74 and 0.76, respectively. In context of Fuzz-AHP the accuracy of 1.08, 0.88 and 0.94 were obtained. While, the accuracy of Fooler Hierarchical Triangle were obtained 0.78, 0.84 and 0.91, accordingly.

As results indicated integration of Fuzzy-AHP represented more accurate results. Results of this research are great of important for future research in context of methodological issues for GIScience by means of identifying most efficient methods and techniques for variety of applications such landslide mapping, suitability assessment, site selection and in all for any GIS-MCDA application.

The Khanik-Ghazan Titanium ore deposit is located at 82 km northwest of Urmia, northern Sanandaj-Sirjan zone. The main objective of this research is to identify potentially mineralized areas and to prepare a mineral prospectivity map in the Khanik-Ghazan deposit applying the Fuzzy Inference System (FIS). After preparing the facto maps, the main stages of the investigation comprise the preparation of fuzzy factor maps using the appropriate linguistic variables and proper membership functions, combining factor maps using the fuzzy inference (by creating a fuzzy database of If-OR rules), identification of susceptible areas, and the generation of a potential mineral map using the output closure. In this study, in order to control the accuracy of the data, we tried to apply two new integrated methods including the fuzzy logic and hierarchical analysis processes. The results obtained from these methods was confirmed and complemented by each other and demonstrated highly potential mineralized zones. This statement is validated by several investigation methods including the field surveys and evidence of 80 samples collected from rock outcrops. Based on obtained results and modelling of geophysical data, the central part of the study area was recognized for further exploration using the drillcore subsurface exploration.

Ionosphere is a layer in the upper part of the atmosphere wide-ranging from 60 km to 2000 km. It has a very significant role in radio wave propagation because of its electromagnetic attributes. Ionosphere is mainly affected by solar zenith angle and solar activity. In the day-time, ionization in ionosphere is at the highest level and the ionospheric effects are stronger. In the night-time, ionization decreases and the effects of ionosphere gets weaker. One of the most important parameters that defines the physical structure of ionosphere is Total Electron Content (TEC). TEC is a line integral of electron density along signal path between satellite to the receiver on the ground. The unit of TEC is TECU and 1 TECU equals 1016 electrons/m2. The TEC values can be computed from dual frequency Global Positioning System (GPS) stations, which are the most available observations for studying the Earth’s ionosphere. However, because of scattered repartition of dual frequency of GPS stations, precise information on TEC over the favorable region is unknown. Fuzzy inference systems (FIS) take inputs and process them based on the pre-specified rules to produce the outputs. Both the inputs and outputs are real values, whereas the internal processing is based on fuzzy rules and fuzzy arithmetic. FIS is the key unit of a fuzzy logic system having decision making as its primary work. It uses the “IF…THEN” rules along with connectors “OR” or “AND” for drawing essential decision rules. A FIS is defined according to the following five main sections:• Rule Base − It contains fuzzy IF-THEN rules; • Database − It defines the membership functions of fuzzy sets used in fuzzy rules; • Decision-making Unit − It performs operation on rules; • Fuzzification Interface Unit − It converts the crisp quantities into fuzzy quantities; and • Defuzzification Interface Unit − It converts the fuzzy quantities into crisp quantities. In this paper, the TEC of the ionosphere is modeled using FIS. The fuzzy inference system uses the rules IF-THEN to recognize the characteristics of dynamic phenomena. This feature, along with the simplicity of computing, has made it possible for this model to study the temporal and spatial variations of the ionosphere. In fact, the main innovation of the paper is the time series modeling of TEC in Iran using FIS. Hybrid particle swarm optimization training (BP-PSO) algorithm is used to train fuzzy network. This algorithm uses the PSO in the early stages of searching for solution and uses the back propagation (BP) near the optimal solution. From the observations of 2015, the Tehran GPS station, which is one of the IGS global stations, was used for evaluation of the proposed model. Also, the results were compared with the results of the global ionosphere map (GIM) TEC as well as artificial neural network model (ANN). In order to evaluate the accuracy of the fuzzy model presented in this paper, 5 days of each season were selected as the test data and model validation was performed in these 20 days. Based on the results, the average relative error calculated in the 20 test days for FIS, ANN and GIM models compared to GPS were 11.25%, 19.68% and 16.03%, respectively. Besides, the average absolute error calculated for FIS, ANN and GIM models compared to GPS in the 20 test days was 1.32 TECU, 3.33 TECU and 1.98 TECU, respectively. The calculated correlation coefficients between TEC obtained from FIS, ANN and GIM compared to GPS were 0.9474, 0.6960 and 0.831, respectively. The results of the analysis show that the FIS model is superior to the ANN and GIM models. Using the proposed model of this research, the time series of the ionosphere TEC can be modeled and investigated with high accuracy. This model can also be a good alternative to the outputs of the IGS network in Iran.

Groundwater system studies to understanding its behavior, requires the exploratory drilling wells, pumping test and geophysical experiments, which can carried out with most cost. For this reason, simulation of groundwater flows by mathematical and computer models, which is an indirect method to groundwater studies, is being spent a few costs. In this research, the efficiency of artificial neural network, fuzzy logic and time series models has been investigated in groundwater level estimation of Ajabshir plain. Parameters of precipitation, temperature, flow rate and water level within time period of the previous month were used as input and the water table in each period were selected as output through monthly scale (2007-2018). To evaluating the performance of models, Correlation coefficient, root mean square error and coefficient of mean absolute error were used. The results showed that the Fuzzy Logic model are able to estimate water levels with acceptable accuracy. Gaussian functions was a membership functions of fuzzy model in groundwater level prediction, which fitted to the classified data and the output membership function of the sageno model is a function which is based on the inputs. In terms of accuracy, fuzzy logic model with the highest correlation coefficient (0.96), lowest root mean square error (0.068 m) and mean absolute error (0.056 m) was recognized as a best the model in the groundwater level prediction.

One of the natural disasters that plays a major role in the vulnerability and destruction of rural settlements is earthquake. Vulnerability has a complex concept so that involves various factors and variables and using modern methods could improve vulnerability assess of the levels. The objective of this study is to vulnerability assess the levels and prioritization of rural settlements with the use of operators based on Fuzzy Logic. The Fars province was selected as a case study because of exist many major and minor faults, during the years occured huge earthquakes and theirs rural settlements have a variety of natural and human vulnerability. Because of the physical characteristics, rural areas exposured to high vulnerability is compared with urban areas. In this study, for vulnerability assess the levels and prioritization was used fuzzy operators that including multi criteria aggregation criterion importance and orders weight and two important properties consider to qualitative (linguistic) variables and according to various interest groups and also led to better results. Therefore, by using and aggregating various variables and scenarios, areas with high degrees of risk identified. Then rural settlements exposure risk resulting from relatively pessimistic scenario of natural vulnerability was choiced. Eventually, the human vulnerability of settlements have been prioritized using a moderate scenario. The results in all scenarios showed that South and West areas with high values of Peak Horizontal Acceleration, new geological formations and proximity to active faults as areas with high degrees natural vulnerability of risk has been identified and always happening huge earthquakes but from the perspective of human vulnerability variables is moderate.

Considering Iran’s situation in drought risk area, in this study karstic waters are investigated. The study area is located in central of Iran, Semnan province. Several factors are important in Karstification and formation water resources in carbonate, among them important are petrology, topography, climate, geomorphology and hydrogeology are pointed out. In this research, the role of structural factors in the development of karstic water resources in Semnan province has been studied. firstly, information layers of each factor prepared. For example, tectonic elements, includes maps: lineament density, faults length density, faults intersection density and density distance from faults making use geological maps and processing of satellite images. Information layers analyzed in the geographical information system (GIS), Expert Choice Software making use geostatistical methods. In this study, multi methods like analytic hierarchy process (AHP) and Fuzzy analytic hierarchy process (Fuzzy AHP) used in study of karstic water resources.

Complete recognition of exploration criteria is considered as an important step in the systematic and scientific analysis of mineral potential mapping. The starting point of this analysis is recognition of the mineral potentials and the mechanism of their formation in different geological areas. In order to achieve such goals we can benefit from the known mineralization information. Integrating and analyzing all the data in the geographic information systems (GIS) can lead to the identification of promising mineral areas for future studies (San Soleimani et al., 2011). GIS can be used to organize, process, analyze and integrate the results of geological, geochemical and geophysical, tectonic and alteration studies in order to identify and evaluate the potential of different minerals (Bonham Carter, 1998). The aim of this paper is to apply the fuzzy integration method in GIS with using all the information, without the need to simplify them, in less time and with acceptable accuracy in order to recognize mineral potentials in the Ramand region. Investigation of mineral potentials in this area with using fuzzy logic in addition to the scientific-research aspects, can provide the discovery of appropriate patterns for other mineral resources in Iran.

The first step in any exploration project is that mapping is done with the aim of optimal potential to provide a model for the exploration of possible deposits in the region. This process in next steps will be continued with an extraction of useful information from various data that are contained in the database and this done work is done based on the properties of the elements under prospecting and the relevant exploration models. However, in the final stage, the combination of these maps is based on different models (Bonham Carter, 1998). The pre-requisite for the production of the mineral potential map, is to determine the weight and value which reflects the relative importance of the data classification (Hosseinali and Alesheikh, 2008). According to the study's default that is modeling with using fuzzy logic, a range of values between zero and one can be used to express the degree or the value of a collection (Novriadi and Darijanto, 2006) This is such that zero means lack of full membership and value of one is meant to be a full member of the collection. So, other set members can also be allocated values between zero and one and based on the degree of their membership to the set. So, it can be said that in the mining exploration, membership values are used to indicate the relative importance of each map as well as the relative importance of each class of a single map (Bonham Carter, 1998). Exploratory layers produced in GIS (in the fuzzy theory section), appear in the role of fuzzy sets and are combined by fuzzy operators (Table 1). The combining layers in the fuzzy method express the concept from the optimal options.

In this study, some of the geo-referenced data consisting of geological data, remote sensing, tectonic and geophysical data have been used. Geological data for separate lithological units in the region and the identification of faults, remote sensing data to identify hydrothermal alterations and fault lineaments and airborne geophysical data in order to detect magnetic fault lineaments and check the changes of magnetic susceptibility related to hydrothermal alterations are used so that after collecting and entering data, processing the necessary studies has been carried out on them. Finally, by combining the results of each layer and giving the fuzzy weighting to them based on their importance in mineralization and the use of the fuzzy algorithm and gamma (the fuzzy operator) in GIS, the final map is obtained to identify the potential areas with using it in the Ramand region. The possibility to acquire the exploration pattern of base and precious metals is provided in the mineral-prone areas.

In this study, there was a lot of exploration of information such that the decision to determine the potential points and continuing the exploration activities had been made very difficult. Thus, with use of the fuzzy integration method in GIS, all of the information were managed without any need to simplify them, in less time and with acceptable accuracy. Fuzzy logic is a method based on expert knowledge that is used for integration of exploration data and producing the optimal potential map of the Ramand regional reserves. It points to promising and priority areas for accurate and detailed information. Therefore, it is better to carry out exploration operations and reduces the cost and time as well as expedition to decision-making. And accuracy is very effective.

Today Ground water is the main source of drinking, agriculture and other uses for humans. The demand for this critical and strategic natural resource increased with population growth and development of society. This increasing has been declining water resources and damage aquifers environment. Therefore, we need to manage aquifers and understanding the hydrogeological parameters to deal with the water crisis and prevent the distraction of aquifers. The one of most important parameter is hydraulic conductivity. Although, the ground water system is a complex system and estimation of hydrogeological parameters is associated with inherent uncertainty and also is costly and time consuming that usually done with classical methods such as laboratory tests, slug test, tracing test and pumping tests. So recently use artificial intelligence methods for estimation of hydraulic conductivity, reduced the uncertainty of this parameter and it adds up some accuracy. So that it can overcome on the shortcoming of classical methods. In this study, four artificial intelligence methods; mamdani fuzzy logic(MFL) system, sugeno fuzzy logic(SFL) system, Wavelet-neural network method and Least square support vector machine(LS-SVM) method were used as individual models to estimate the hydraulic conductivity by using of surface geophysical data in Maragheh-Bonab aquifer. Given that each these models based on their inherent properties, they presented good results in some parts of area. Therefore, for concurrent use of performance of all these models the nonlinear combination method as a supervised committee machine artificial intelligence (SCMAI) model were used to estimate the hydraulic conductivity in maragheh-bonab aquifer. The result of this model showed that this new combinational model has high performance than other single models that presented by using different evaluation criteria. Therefore, this model could also be used for estimation hydrogeological parameters in areas with high complexity. The SCMAI model was tested against 15 data. The RMSE and for SCMAI prediction were computed as 0.045 and 0.97, respectively. Comparing the error measure values with dose of individual models above, it is seen that SCMAI outperforms individual AI models with low RMSE and high values. This result implies that SCMAI model shows high performance for estimation the hydraulic conductivity values in the heterogeneous unconfined aquifer in Maragheh-Boanb plain.