Geographic Information System (GIS)
Mohammad Karimi; Parastoo Pilehforooshha; Ali Safari
Abstract
Extended Abstract Introduction:The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, ...
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Extended Abstract Introduction:The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, 1994; Carranza et al., 2008a). According to the investigations, the common models of map integration that are used in the discovery of mineral reserves in the initial exploration stage include index overlap model, fuzzy operators, weighted indicators and smart methods such as random forests and artificial networks. Determining the values of weights and scores that show the relative importance of the effective factors is the primary requirement in combining the maps and preparing the mineral potential map (Agterberg, 1992; Brown et al., 2000).The purpose of this research is to prepare a potential map of copper deposits in Dehj-Bazman region using two methods of random forest and support vector machine. In addition, in order to compare the potential map of porphyry copper reserves resulting from the random forest method, the support vector machine method and the knowledge-based methods of index overlap and fuzzy logic were used.Materials & Methods:The area studied in this research is a part of the magmatic belt of Kerman region, known as the Dehj-Sardouye belt. The information layers controlling mineralization in Dehj-Bazman area include rock units, structures, alterations, geochemistry, geophysics and copper deposits. In practical applications of machine learning algorithms, mineral potential mapping is essentially a bimodal classification problem, such that each undiscovered area is classified as prospective or non-prospective according to some combination of mapping criteria (Zuo, 2011). The final results are a set of predictive maps that show target areas with high ore formation potential.In order to model, training was done. Before training the random forest model, the input data set and the target variable should be prepared and then the model should be trained. The target variables for entering the random forest model and support vector machine were determined as deposit points (values of 1) and non-deposit points (values of 0). Then the genetic algorithm was used to adjust the parameters.Evaluation of the predictive performance of random forest model and support vector machine can be described by the ambiguity matrix. In this matrix, there are four components, which are defined as: (1) a deposit sample that is correctly classified as a deposit (TP); (2) a deposit sample incorrectly classified as a non-deposit sample (FN), (3) a non-deposit sample correctly classified as a non-deposit sample (TN), and (4) a non-deposit sample that is wrongly classified as a deposit sample (FP) (Liu et al., 2005; Tien Bui et al., 2016): (8) (9) (10) (11) (12) After training and evaluating different models, the best model was obtained by adjusting different parameters and it was used to integrate factor maps in order to predict areas with high potential of porphyry copper deposits. Also, knowledge-based methods of fuzzy logic and index overlap were used to combine factor maps to compare with the results of intelligent methods.Results & Discussion:At this stage, the desired information layers were collected and prepared in the GIS environment, and then factor maps were prepared. Accuracy, sensitivity, specificity, predicted positive value, predicted negative value, kappa index and OOB error were used to evaluate the performance of random forest model and support vector machine. Also, the importance of the predictor variables in the random forest model was evaluated through the mean decrease in accuracy and the mean decrease in node impurity or the Gini impurity index (Breiman, 2001). According to the results, the most important predictor in the random forest model is the geochemical map, while the structures factor has the least impact in predicting the preparation of the mineral potential map with the final random forest model.In the potential maps of porphyry copper deposits obtained from two methods of random forest and support vector machine, the target areas cover 14% of the studied area, in which there are 92% and 87% of known deposits, respectively. Finally, the efficiency of machine learning methods and knowledge-based methods were compared. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. For this purpose, weights of 0.3, 0.25, 0.25, 0.1, 0.1 were assigned to produce maps of alteration factor, geochemistry, geology, geophysics and structures respectively. In the potential map obtained from the method of index overlap and fuzzy logic (fuzzy sum), the areas predicted as copper mines cover 16 and 17 percent of the studied area, respectively, in which 83 and 79 percent of the existing mines are located.Conclusion:This research was conducted with the aim of evaluating and comparing the effectiveness of random forest method and support vector machine method and knowledge-based methods to prepare porphyry copper potential map of Dehaj-Bozman region of Kerman province. Based on the results, the random forest model works well in the field of porphyry copper potential map preparation with geochemical, geophysical, geological, alteration and structures datasets. In addition, the random forest algorithm can estimate the importance of factor maps.The results of this research show that the geochemical factor map is the most important and the structure factor map is the least important in predicting the data-driven model of random forests. This estimate of importance is consistent with geological knowledge about porphyry copper mineralization in Dehj-Buzman region. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. According to the obtained results, the performance of the random forest model is better than the vector machine model, and also, the performance of the support vector machine model is better than the knowledge-based methods.
Seyedeh Sareh Dabiri; Mohammad Taleai; Ghasem Javadi
Abstract
Introduction
The study of the areas with geothermal energy potential is of particular importance in realizing the goals ofsustainable development. Areas with geothermal potential areof great importance in terms of application as renewable energy resources, tourist attraction, greenhouse construction, ...
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Introduction
The study of the areas with geothermal energy potential is of particular importance in realizing the goals ofsustainable development. Areas with geothermal potential areof great importance in terms of application as renewable energy resources, tourist attraction, greenhouse construction, etc.Generally, in geothermal exploration projects, studies are initially carried out with regard to the existing indicators, and the outcome of the primary location is used for more detailed studies. The identification of the areas with geothermal potential, which is the first phase of geothermal energy exploration, is complex and difficult.
Determining areas with geothermal energypotential as a basis for clean and environment friendly natural energyexploration studies, is important for achieving sustainable development. The purpose of this paper is to identify the areas with geothermal potential with regard to the characteristics of the northwest regions of Iran and the application of Geospatial Information Systems and Multi-criteria analysis methods, which have many advantages in the field of exploring the regions with geothermal potential.
In this study, the spatial Multi-criteria analysis package of ILWIS software and also the decision-making method based on the Ordered Weighted Average (OWA) in TerrSet(IDRISI) software have been used
Different scenarios of decision-making were implemented in the case study area and, the results were compared with the location of hot water springs in the region. The results indicate that the location of the determined sites is close to the hot water springs, which confirms the results of the proposed model of the paper.
Materials & Methods
The study of geothermal energy with the help of the spatial information system has drawn the attention in recent years. The purpose of this paper is to study areas with geothermal potential in the northwestern regions of Iran. These regions have different effects on the Earthand the researchers of this field use these effects to find new methods for measuring geothermal resources (Yousefy, 2006). Nowadays, GIS-based MCDM techniques are effectively used in these types of studies. Therefore, it has been tried to use some of these techniques in this research. In addition to the novelty of the topic of the geothermal studies in Iran, the issue of modeling different decision-making scenarios has been taken into consideration fromthe pessimistic view (with low risk) to the optimistic one (with high risk). Therefore, in this research,areas with geothermal potential have been identified and compared, with the help of study with the help of spatial data and Multi-criteria decision-making methods. In this study, decision-making criteria are evaluated and selected usinglibrary studies from previous researches. Also, based on the weighting methods and the integration of criteria, 8 scenarios were produced and their results were compared with each other. Meanwhile, the weight of the criteria was calculated using questionnaires and the analytic hierarchy process (AHP) method. The Ordered Weighted Average (OWA) method was applied to create various scenarios. Figure-1 shows the stages of this research.
Results & Discussion
The two software (ILWIS and TerrSet), provide powerful tool for standardizing, weighting and integratingthe standard maps associated with the decision-making process. In the implementation stage, the maps are standardizedafter the preparation of thestandard maps in the acceptable format of each software. In this study, fuzzy and AHP methods were used for standardization and weighting,respectively. Finally, the input factors are integrated according to different scenarios. The results are shown in Fig-8. In order to evaluate the results, the geothermal map produced based on the model proposed in this article has been compared with the location of hot water springs. The results of most scenarios show that, hot water springs are generally located in two classes with high suitability which confirms the results of the research. In Fig-9, hot springs are located in the classes with high suitability, as it was expected. This means that the results of this research are acceptable. Adaptation and compatibility of the geothermal map and the existing situation provide the possibility of using the results of the case study area in the exploration studies of other regions.
Conclusion
In this research, multi-criteria decision-making based on the use of GIS tool was used as a feasibility study in the first phase of geothermal exploration. The layers were processed and using theAHP-OWA integration methods in the 8 scenarios, they were integrated and the obtained results were investigated and compared. In most scenarios, hot water springs are in suitable or very suitable classes. This reflects the acceptable results obtained from the proposed modeling of this research.
Abolfazl Ranjbar; Farshad Hakimpour; Siamak Talat Ahary
Abstract
Extended Abstract
Introduction
The problem of locating bank branches is classified asNP-Hard problem which can possibly be solved only in exponential time by the increase in the number of banks and the large number of customers, especially when the location model includes various datasets, several ...
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Extended Abstract
Introduction
The problem of locating bank branches is classified asNP-Hard problem which can possibly be solved only in exponential time by the increase in the number of banks and the large number of customers, especially when the location model includes various datasets, several objectives and constraints. As a consequence, we need to use heuristic methods to solve these types of problems. Also, since majority of data and analyses applied in the locating problems are spatial; GIScience’s abilities should be employed besides optimization methods.
Nowadays, to perform particular financial tasks, bank customers often need to be present at their bank. For the sake of its customers, a bank should increase its branches in the city to attract more customers in the race with competing banks. However, establishing new branches is too expensive and banks prefer to carry out an optimal location finding procedure. Such procedures should consider many criteria and objectives including spatial data of customers, new and existing bank branches as well as the level of attraction of banks. Customers often select a bank that is closer to them, has better services or financial records and also consider other human or physical factors. Hence, planning to increase the number of customers for a new branch of a bank considering spatial criteria and various other objectives appears necessary.
Materials & Methods
This paper determines the location of bank branches. Finding an optimum site for branches depends on many factors and these problems are known as NP-hard problems. Despite being approximate methods, meta-heuristic algorithms seem suitable tools for solving NP-hard problems. In this paper, Grey Wolf Optimizer (GWO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Cultural Algorithms (CA) and Invasive Weed Optimization (IWO) are applied for finding the best location for bank branches. From marketing point of view, the aim is to attract more customers while the number of attracted people to a new branch should be acceptable. The new methods have capability to find the optimum location for new branches. The location of a new branch should be as far away as possible from branches of the same bank. The other condition is that the total number of customers for the new branch should not be less than a specified number, while the new branch should not attract customers of old branches of the same bank. To fulfill this propose, a part of the city of Tabriz was selected for implementation.The assumptions for the defined problem can be expressed as the following statements:
a)We consider four different banks (Melli, Mellat, Sepah and Mehr) in our study area.
b)Population density (of people over 15 years of age) is available at the building block level.
c)Banks have infinite capacity for accepting customers.
d)Each customer refers to only one bank.
e)New bank branches should have maximum distance from the branches of the same bank, so that, it attracts minimum number of customers from branches of the same bank.
Conclusion
To evaluate the quality and accuracy of the algorithms, several iterations are performed. The results of statistical and final tests indicate that the accuracy and convergence speed of Invasive Weed Optimization are more than other Algorithms in finding optimal location of bank branches.
Mohammadzaman Ahmadi; Saeed Behzadi
Abstract
Abstract
Wells are one of the main sources of drinking water, agriculture and industry. Water quality in terms of drinking is the most important parameter among qualitative parameters. Therefore, the investigation and anticipation of pollution are the goals of managers and planners. In this research, ...
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Abstract
Wells are one of the main sources of drinking water, agriculture and industry. Water quality in terms of drinking is the most important parameter among qualitative parameters. Therefore, the investigation and anticipation of pollution are the goals of managers and planners. In this research, artificial neural network and geospatial information system have been used to determine the contamination of magnesium parameter in the water of Gonbad villages in Golestan province during the 4 consecutive of 2008, 2009, 2010 and 2011. In this model, the artificial neural network has been evaluated in Perceptron structure with a number of hidden layers and various neurons. At present, pollution of underground is increasing due to the chemical and industrial activities. Therefore, it is necessary to identify vulnerable areas to prevent the pollution of groundwater. Also, in this research, to determine the groundwater contamination, maps such as topography, geology, location of wells, slopes and …, were used in spatial environment. After determining the amount of contamination using the neural network models and the output of the model in spatial environment, the pollution maps were obtained. Also, by observing contamination maps and data available in the aforementioned years, it can be concluded that the level of pollution was low and this pollution cannot be dangerous.
Farzad Foroozani; Mohammad Reza Malek; Ali Esmaeily
Abstract
Abstract
The Distribution networks are the most important part of the utilities that distribute electrical energy to the consumers. Problems with location-referenced information such as inaccuracy, inability to control information, and lack of rapid access to information are considered as technical ...
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Abstract
The Distribution networks are the most important part of the utilities that distribute electrical energy to the consumers. Problems with location-referenced information such as inaccuracy, inability to control information, and lack of rapid access to information are considered as technical problems. The complexity of updating information, the complexities related to information storage and wearing out are no exception to this rule. Existing technical problems and the failure to use the new systems in the relief issue will prolong the duration of the blackout. The purpose of this research is to design and implement a context aware spatial information system for providing a series of services such as routing, map displaying, and the provision of distribution network information in the field of urban electricity distribution incidents. Urban electricity distribution networks consist of various parts and equipment. The rescuer determines the type of failure due to available and accessible network information. The failure type is considered as the user's environmental context, and the location of the rescue vehicle is considered as the location context. Therefore, the context in this study are classified into two general categories of position and network context. Finally, the implementation and testing of a designed to help managing the urban electrical distribution networks was studied which resulted in 80% satisfaction.
