@article{ 
author = {Habibi, R. and Alesheikh, A. A.},  
title = {Quality of Spatial Interpolation Services Assessment for Development of Air Pollution Monitoring Systems Based on the Internet of Things}, 
abstract ={The Internet of Things (IOT) is a concept which emerged simultaneously with developments of data acquisition and sharing technologies, and growth of Information and Communication Technology (ICT). It also quickly involved different aspects of human being life in the modern world. In this study, the problem was investigated of environment monitoring issue, in particular air pollution.  Air pollution is one of the main problems of the big cities and its harmful effects on environment and public health is inevitable. In this regard, real-time and optimal monitoring of air pollutants concentrations plays a significant role in inhibition of this problem. In this study, by integration of GIS and Internet of Things (IoT), guidelines were presented for optimal management of air pollutions High temporal and spatial variability of pollutants implies importance of using in-situ sensors to monitor this phenomena. Deployment and maintenance of air pollution sensor networks are very expensive. Moreover, measurement of air pollution concentration at anyplace is also impossible practically.  Considering air pollution monitoring as a scenario in IoT world, accessing concentration level of air pollution at right time and right place, and due to client request is necessary. Therefore, a real-time monitoring system using geoprocessing services is needed which estimates concentration level of pollutants throughout the city. Currently, air pollution monitoring network of Tehran is governed by two organizations, while better estimation of air pollution requires both network data. Another problem is the heterogeneity of this network which make difficult to expand this measurements into another scenarios and projects, and transform information between IoT components in IoT context. Providing interoperability in this network, a spatial data infrastructure (SDI) is required to close these gaps. For this purpose, OGC Sensor Web Enablement (SWE) standards including Sensor Observation Service (SOS), Sensor Model Language (SensorML) and Observation and Measurement (O&M) were used. In proposed monitoring system, RESTful web services in Service Oriented Architecture (SOA) were implemented. Providing reliable information for users in the shortest possible time is another challenge in the Internet of Things. Accordingly, reliability and time as two indicators for evaluation quality of geospatial web services were proposed. Thus, quality of four interpolation services were investigated by utilizing two deterministic methods (Inverse Distance Weighted (IDW) and Global Polynomial Interpolation (GPI)), and two geostatistical methods (Empirical Bayesian Kriging (EBK) and Ordinary Kriging (OK)). In this study, quality behavior of interpolation web services was examined via study of the six parameters impacts on qualitative indices. Results of this analysis were independent of the measurements data type and could be used in other sensor networks. Among these six parameters, four of them were related to spatial distribution and network structure including average, minimum and maximum distance between in-situ sensors and also number of stations. 2 other parameters were average and standard deviation of sensor measurements data that indicate statistical characteristics of sensor measurements data. Impact of mentioned parameters on indices were investigated by Pearson correlation coefficient.  Results showed that average RMSE of OK and EBK services, 61.14 and 11.15 consecutively, were better than two other methods in reliability index. Time index of EBK service was weak (6 min 21 s) but other services were favorable (less that 1 s). Reliability index was impressed under the direct statistical properties (rRMSE > 0.9). In contrast, time index was more impressionable than structural parameters especially number of sensors (rIDW=0.99, rEBK=0.97 and rOK=0.66). Proposed solution and results would be so useful in environmental monitoring systems development and interaction with other components in IoT context.},  
Keywords = {Internet of Things, Interpolation, Sensor Web, Air Pollution},
volume = {6},
Number = {4}, 
pages = {1-15}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-539-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-539-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {GhasemiNejad, R. and AliAbbaspor, R. and Mojarab, M.},  
title = {Extraction of Seismic Patterns using Fuzzy Clustering Algorithms Optimized by Particle Swarm Optimization}, 
abstract ={As a common method in data mining, pattern recognition within seismic data using clustering leads to the extraction of valuable information from large databases. Categorization of clustering algorithms is neither straightforward nor canonical. Clustering algorithms can be divided into four broad classes of hierarchical, density-based, grid-based, and partitioning methods. The application of these methods depends on the kind and nature of problem. From the labeling and assignment point of view, clustering algorithms can be divided into hard and soft methods. In the hard clustering, each data belongs to one and only one cluster while in soft (or fuzzy) clustering; each data belongs to different clusters with different degrees of membership. In the field of seismology and with application of hazard analysis, it is an essential task to break an area into different regions with more or less similar seismological characteristics. So it is needed to use clustering algorithms. For data mining and clustering analysis among seismic catalogs, some issues should be considered, such as, among an active seismic area, there are different regions with different rates of seismicity, As a result, the density and number of events are not the same in different regions or seismotectonic provinces, the earthquake events are mainly distributed among different segments of major faults, there are different seismotectonic regions among an area, therefor seismic characteristics in a region vary gradually and there are not abrupt changes in these characteristics. Thus, it may be a more proper approach to partition earthquakes based on the fuzzy clustering methods that tend to investigate realistic data. Although many clustering algorithms have been proposed so far, these algorithms are very sensitive to initial conditions and pretty often get trapped in local optimum solutions so they couldn’t find real clusters in space of problem. Therefore, some other global optimal searching algorithm should be used to find global clusters. The clustering problem may be considered as an optimization problem in general. Metaheuristics are widely renowned as efficient approaches for many hard optimization problems including cluster analysis. Metaheuristics uses an iterative search strategy to find an approximate optimal solution using a limited computation resource, such as computing power and computation time. Therefore, the present paper suggests some metaheuristics algorithms to solve the problems associated with clustering algorithms, Gustafson Kessel and Fuzzy c-means. The two algorithms called PSO-GK and PSO-FCM, respectively then they are applied on synthetic seismic data as well as real seismic data acquired across Iran, with the results validated using validity clustering indexes such as fuzzy hyper volume (FHV), average partition density (APD) and partition density (PD). These indexes show the clear separation between the clusters, minimal volume of the clusters, and maximal number of data points concentration in the vicinity of the cluster centroid. A low value for FHV and high values for APD and PD indexes would ideally indicate a good partition. The amount of FHV index in PSO-GK algorithm for synthetic seismic data is 0.4272 and for real seismic data acquired across Iran is 0.0941 better than this index in PSO-FCM algorithm. The two other indexes are also achieved better amounts in PSO-GK algorithm than PSO-FCM algorithm. Based on the comparison results, the proposed Gustafson-Kessel approach-based algorithm was found to be more appropriate for the analysis of seismic data.},  
Keywords = {Particle Swarm Optimization, Fuzzy c-means, Gustafson Kessel, Seismic Analysis},
volume = {6},
Number = {4}, 
pages = {17-28}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-486-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-486-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {RaoofianNaeeni, M. and Malekshahian, Z.},  
title = {Geometric Deformation Analysis of the Earth’s Crust using GPS Observation and Non-linear Finite Element Method based on C1 Bezier Cubic Interpolation}, 
abstract ={Earth as a dynamic system is constantly undergoing the Change and variation. The changes causing factors include a wide range of dynamic processes, which consist of tidal effects, the loading on the crust and tectonic activity of tectonic plates. Accumulation of strain energy at the boundary between the plates and the surrounding area of faults are the most important consequences of movement and deformation of the tectonic plates. This energy may causes tension forces in the fracture sits, and if these forces can overcome the rocks resistance, an earthquake will occur. To measure strain energy, some mechanical tools contain strain gauges and turnkey have been installed at faults location which measure the deformations in situ. Due to the high cost of purchasing and maintenance of such these devices, based on geodetic observation, some alternative methods have been developed for study of the earth crust in local scales. Today, time measurement of the earth's crust deformations has been recognized as one of the notable branches of geodesy. Measuring the Earth's crust deformation by geodetic methods has significant impacts on geological studies and provides a deep insight into understanding the mechanism of  tectonic activities such as earthquakes and volcanoes. Over time, measurement methods in geodesy significantly expande and space measurement methods have been replaced classic geodetic observations. The Global Positioning System (GPS) playes an important role in the development of observational methods in geodesy. By using GPS observations, determining of three-dimensional positioning become possible in a Earth-fixed coordinate system. Therefore, by comparing the obtained positions in different epochs, displacement vector can be calculated. Displacement vectors are not suitable quantities for deformation geometric interpretation, because they must be determined in comparison with a constant basic coordinate system. On the other hand, strain tensor can be calculated by using displacement vectors. This tensor contains invariant parameters of coordinate system and has a direct association with geometric interpretation of deformation. Since Iran is a seismically active and has been located within the convergence zone of two rigid plates, the Arabian and Eurasian plates, It is known as a natural laboratory for studying the kinematics and dynamics of tectonic interactions. Considering these reasons, researchers aim to study the geodynamic network of Iran. The neotectonics of the Iranian plateau is complicated due to various tectonic processes, (Zagros, Alborz, Kopeh Dagh, and Talesh), subduction of the oceanic crust (Makran), and a transition zone between the Zagros fold-and-thrust belt and the subduction zone of Makran. In this paper, using observations of GPS velocity vectors at the different station of Geodynamic network of IRAN, two-dimensional deformation of the earth crust was estimated by 2-D strain analysis within 2009 -2013. And then, in order to deformation interpretation, invariant geometric criteria like dilatation and maximum shear were evaluated. In this regard, as the first step, coordinates of the all GPS stations was calculated in two observational epochs in global coordinate system. In addition, considering the proper  map projection, the coordinates were transfered into the projection plane. In the next step, the apparent displacement vector was determined for each GPS station. Finally, using displacement vectors, strain tensor components as well as dilatation and maximum shear invariant parameters was computed for the whole Geodynamic network. Strain tensor calculates by using displacement vector derivatives but geodetic observations, only provide the discrete values of the displacement in GPS stations. Using the data interpolation techniques such as finite element method is an appropriate way to solve this problem. In this method, the domian of the data point partitions into some smaller sub-domains and in each one, a smooth function fits in that domain by considering the specified constraints on the existing data point. In order to create a smooth surface through the data points there must be a smooth transition from one patch or element to another across all shared edges. Interpolating functions in each sub-domain must apply in some continuity conditions such as continuity of the function itself, the first or higher order derivative when passing between two adjacent elements. Due to the nature of the Earth's crust and two-dimensional strain analysis; triangular element is the simplest geometric element in this study. In addition to determine the mathematical form of displacement function using discrete values of displacement, nonlinear finite element method was known as one of the most important point of this study. In this method, after an area meshing by Delaunay Triangular Element, C1 continuous interpolation was adopted. The interpolating function (displacement vector) on each triangle was a rational function gained by blending three cubic polynomials that are known as the Cubic Bezier Triangular Patches. Thus, unlike the previous methods, in this case, the displacement vector in common boundary of the two adjacent Triangular elements had C1 continuity, so a smooth interpolation having C1 continuity in the entire GPS network (vertices of the triangle domain) is achievable. Since we are working with triangles, we will utilize barycentric coordinates rather than Cartesian coordinates. So the Bezier Triangular patches (displacement vector) or Bezier triangles were defined by Bezier ordinates or control points in barycentric coordinates form. For our interpolating problem, only numerical values of displacement vector at the three vertices of the domain triangle was provided. So the inner control points of the control mesh (unknown Bezier ordinates) must be determined. This can be done if information about the gradient or normal on the control points is given. Least Squares Minimization Method based on Finite Difference Techniques was helpful for prescribing the first order derivatives of displacement function at the vertices of each triangle for creating a C1 surface. The number of vertices around the data point is chosen by the minimum distance technique. According to the conducted analysis, the maximum amount of shear quantity equals to 5.58×10-5 unit/year value in the southern parts of Iran. The maximum amount of dilatation quantity equals to the -2.89×10-4 unit/year value in the southeast of the country, which represents a net contraction in these areas. This contraction demonstrates the collision of the Arabian plate from the southwest to Eurasian from the Northeast, and confirms the reverse and strike-slip faults in the Iranian plateau. Also the result of the computation and the evaluations by comparison with the seismic map of the region show the success and usefulness of the presented method for deformation study of the curst.},  
Keywords = {Deformation Analysis, Nonlinear Finite Element, Maximum Shear, Strain Tensor},
volume = {6},
Number = {4}, 
pages = {29-39}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-534-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-534-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {HosseiniAbbasabadi, M. and Taleai, M.},  
title = {Evaluate the Quality of Urban Life based on the Spatial and Census Data}, 
abstract ={Quality of life is the ability  of environment to supplying and supporting the material and spiritual needs of people, which include concepts such as individual well-being (health, healthcare), social welfare (security, environmental quality, etc.) and spatial equity (access and the same distribution services and urban facilities). Quality of life used to be a key concept and an efficient tools for place ranking, identifying and documenting the causes of class differences in the cities and it have been interest to administrators and urban planners. This thesis aims to assess the quality of urban life on the dispersion of population parameters. For this purpose, three basic parameters for assessing the quality of urban life is considered. These parameters include the socio-economic, environmental and spatial equity. For assessing the socio-economic aspect, census data is used. Pearson’s correlation was computed to analyze the relationships among the variables. Further, factor analysis was conducted to extract unique information from the combined dataset. four factors were identified and interpreted as Housing conditions, Working and literacy conditions, Educational status and activity status and income respectively. Each factor was viewed as a unique aspect of socio-economic parameter of the quality of life. As well as to assess the environmental parameter of quality of life by using satellite imagery and geospatial data some information like  maps of NDVI index, land surface temperature, air pollution and noise pollution were extracted, and final index of  the environmental parameter was obtained by integration of them.. For evaluating spatial equity two indicators, Accessibility and mixed land use was considered. Equal distribution and access to urban services was measured by these two indicators. This evaluation done for the educational, health, commercial, parks, sports, religious and cultural land use. To investigate the spatial distribution of QOL index, Moran's spatial autocorrelation is used. The results showed that the quality of life index in the studied region is clustered and places by similar value are neighbor. Finally, for investigate the relationship between final extracted index for each aspect of quality of life with each other, the Pearson correlation analysis was used. The results showed a positive correlation between socio-economic index with spatial equity (0.543) and environmental index (0.415). Actually making some basic change in these aspect of QOL can result in reducing of difference between QOL of poor and rich zone of city and can supply the basis of improvement of them. Also it can change the situation of equality and justice in all aspect QOL.},  
Keywords = {Quality of Life, Principal Component Analysis, Spatial Equity, Environment},
volume = {6},
Number = {4}, 
pages = {41-55}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-441-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-441-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Mohammadzade, A. and Varesi, A. and Janalipour, M.},  
title = {Presentation of a Method for Detecting Urban Growth using Spectral- Spatial Variation Indicators and Remote Sensing Data}, 
abstract ={Urban growth and its monitoring are important items of interest for urbans and municipalities. Remote sensing and related technologies are new tools that recently used for this purpose. Change detection techniques generally divided into two groups: object based and pixel based techniques. Pixel based techniques included algebra, transformation, fuzzy, hybrid, multi temporal direct comparison and classification based methods. In classification comparison is useful. In these methods quality and quantity of training samples are vital for producing good results. According to research conducted, accuracy of unsupervised classification techniques is less than supervised classification techniques. On the other, supervised techniques need training samples that obtaining these samples is time consuming. As first goal in this research, presentation a supervised method for obtaining training samples automatically. Second goal is using spectral- spatial variation indicators in change detection. Landsat 5 TM images in 1990, 2000 and 2011 were used for change detection of Shiraz city. In this research reference land use map was prepared  by IRS, IKONOS and Google earth images. At first, images were preprocessed. In preprocessing, geometric and radiometric calibration were done. Images were georeferenced by polynomial method and RMSE calculated less than 1 pixel. After preprocessing, spectral- spatial variation indicators were calculated by 3d  wavelet decomposition. These indicators describe spectral and spatial simultaneously. Results showed that spatial variations of natural features are less than spectral variations. So these features are brighter than other features in spectral variation images. Then data were clustered into four classes urban, baresoil, road and vegetation by fuzzy c means method. Overall accuracy of fcm calculated 81.6, 86.5 and 87% in 1990, 2000 and 2011 with consideration variation indicators. Train data were selected for maximum likelihood classification automatically. Overall accuracy of maximum likelihood classification calculated 87.36, 89.5 and 89.7% in 1990, 2000 and 2011 with consideration variation indicators. Results showed variation indicators and automatic selection of training data improved overall accuracy and separation of classes. Classified images used for change detection. Some of samples were selected for assessment accuracy of final product. Confusion matrix found using reference data. Results showed spectral- spatial variation indicators improved accuracy of change detection. At the end support vector machine used in post classification comparison. Produce and user accuracies of this method were obtained 76.5, 65% for first interval and 77.84, 78.23% for second interval under consideration variation indicators. In the event that accuracies of proposed method were obtained 84.63, 78.3% for first interval and 85.89, 88.42% for second interval. So accuracies of change detection with proposed method is higher than support vector machine.},  
Keywords = {Remote Sensing, Spectral-Spatial Indices, Urban Growth, Post Classification Comparison, Landsat5},
volume = {6},
Number = {4}, 
pages = {57-74}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-574-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-574-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Tabasi, M. and Alesheikh, A. A.},  
title = {Modeling Spatial Spread of Epidemic Diseases using Agent-based Simulation (Case Study: Seasonal Influenza)}, 
abstract ={Spatial epidemiology issues are outstandingly important, particularly the viral spread through populated areas is believed to be one of the major concerns. The outbreak of epidemic diseases in a community is inherently a spatio-temporal process of great importance for modern society. Modeling the spread and abrupt transmission of infectious diseases demands a better understanding of its dynamic behaviors to avoid sever consequences by appropriate preventive strategies. Agent Based Modeling (ABM) is one of the innovative technologies for observing the spread of epidemic diseases. Agent-based models allow interaction among individuals and are capable to overcome the limitations of different approaches such as cellular automata and classical epidemic models, permitting to study specific spatial aspects of the spread of epidemics and addressing naturally stochastic nature of the epidemic process. Consisting of a population of individual actors or "agents", an environment, and a set of rules, actions in ABM take place through the agents, which are simple, self-contained programs that collect information from their surroundings and use it to determine how to act. Agent based simulation together with the improved Susceptible-Exposed-Infected-Recovered (SEIR) model, provides an opportunity for the study of interactions at the individual levels that includes social and casual relationships between individuals. The signature success of agent-based modeling in public health is in the study of epidemics and infectious disease dynamics. ABMs have been used to study disease transmission at multiple scales, from individual communities to global pandemics. According to the previous researches, the relationships between factors affecting the outbreak diseases and its spread had not been ccomprehensively presented yet. Therefore, the purpose of this study is to provide a spatial agent based modeling framework for simulating the spread of Seasonal Influenza. Due to the sudden and rapid spread of seasonal Influenza, the parameters of this disease were used for simulation. In this study, to investigate the effects of spatial units and other factors affecting outbreaks of seasonal influenza, simulation was performed, and then analyzed through five different scenarios. These scenarios were presented as the effects of population size, latent period, period of disease, transmission rate and polluted places on the spread of disease. Results showed that the output of epidemic follows a traditional epidemiological curve and also the output of scenarios lead to a better understanding of the factors in the spread of disease. Our results confirms the previous studies on this subject. For example, the results of the  impact of spatial units on outbreak showed that considering the impact of polluted places leading to a significantly increase of pollution in the environment. Therefore, dynamic interactions between agents and environment lead to explore the spread of disease in the model. Finally, the model can be used to inform and educate the public about the spread of infectious disease such as Seasonal Influenza, and can  allow epidemiological researchers to assess systems’ behavior under various conditions. Therefore, This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak.},  
Keywords = {Agent-based Simulation, Epidemic Models, Seasonal Influenza, Spatial Units, SEIR Model},
volume = {6},
Number = {4}, 
pages = {75-86}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-571-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-571-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Kazeminiya, A. and MeimandiParizi, S.},  
title = {Evaluating the Potency of City Crossovers Network with the Approach of Crisis Managing  by using GIS}, 
abstract ={Introduction On account of assessing the intensity and rating of risky urban areas, it is essential to provide vulnerability mapping and location data in connection with land use, population, roads, municipal facilities. One of public spaces, services and strategic location in the city are streets and public ways that must be properly designed and planned. Vulnerability of public ways in a town effectively increase the vulnerability of it. In the phase of returning city to normal situation, communication network has an essential role in the travel and transportation between the residence and workhouse, Transportation of cargo and accelerating the normalization of activities. A crisis management strategies  to deal with the damage caused by natural disasters in cities is consideration of transportation network. Hence, this study evaluates the vulnerability of the communication network against earthquake, in zone 1 of Kerman. After that by designing the geometric network of the roads and Considering the vulnerability of each roads, the best roads is determined to rescue victims of the crisis via analysis of geometric network of the roads in the environment of geographic information system or GIS. Methodology The purpose  of  this study is identification and separation of affecting factors on vulnerability urban streets and quantify the effect of each factor on the level of roads vulnerability. In this research, the ratio of vulnerability in the format of urbanism criteria is evaluated according to previous studies and a list of vulnerable elements of urban communication network is provided. Then the final list is completed and developed by using the Delphi method. Delphi method is used with survey of familiar experts with the issue. Based on the list of vulnerable elements in the format of three main criteria (Table 1) were prepared and after binary comparison with the Analytic Hierarchy Process (AHP) and by using software Expert Choice, The importance (Weight) of each criterion were determined and map of vulnerable roads of study area in GIS environment were prepared by overlapping the (geometric mean) vulnerable layers. Finally, with designing of geometric network of urban streets vulnerability of them in Kerman during and after the crisis occurrence were obtained in communication network analysis to rescue and to transfer victims. Table 1. Effective criteria in vulnerable of urban communication network Sub-criteria Main criteria  proximity to ignite flammable equipment (petrol) proximity to gas equipment proximity to electric equipment Proximity to water equipment Communication networks proximity to urban infrastructure networks  Application density height date criterion buildings overlooking the communication network  Bridges and Underpasses Arcs Number of intersections Street width Length of ways Sleep Features of the communication network  Discussion 1.Mapping the vulnerability of street network   Model of Kerman communication network vulnerability that was used in this study consists of three phases: awareness, design and selection. Awareness Phase In awareness phase, modeling of the vulnerability Region 1 Kerman, and identifying of criteria of vulnerability way in the earthquake were performed via Delphi technique. Design Phase: production of decision options In this study, urban street network in Region 1 Kerman town are decision-making options, which have been provided by maps to a scale of 1: 2000 and have been prepared by applying Aerial Mapping methods (photogrammetry) with land surveying. Optional phase: evaluating of options for decision-making (mapping any of the criteria of vulnerability) After extracting the indices of vulnerability and production options, the final vulnerability of the options must be calculated. The streets network vulnerability of this region of study is dependent to several indices which one of them has a relative importance in comparison with other indices. It is obvious that to calculate the final vulnerability of each way must be determined firstly the weight of indices. Then vulnerability of each way is calculated by calculating the weighted average for each vulnerability index. What is performed at this stage is calculating the weight of indexes and the weighted average vulnerability of each option by using the AHP method. 2.Vulnerability map   Considering the vulnerability is dependent on several parameters that tailoring to each index can be produced the vulnerability map. Finally with overlapping or combination of vulnerability maps can be reached to final vulnerability map. So in the end, the weight of all indices calculating in Expert choice and Excel software, are entered in ArcGIS and by using analytic functions (the intention of using analytic functions were functions overlapping (overlay) that the ARCGIS software was used weighting overlay tools). All maps of vulnerability criteria streets (before phase) are multiplied and combined in their weights.  Thus, the final vulnerabilities map of communication network Region 1 Kerman was obtained. 3.Design and construction of roads in the study area After determining the vulnerability of streets network for emergency services and conveing victims faster during and after the crisis, a geo-referenced database was designed to ways of Region 1 Kerman. At first, a database site called Kerman.mdb created and then three feature classes of data was made that within the database for each dataset were included in it. Then relationships between classes which there are in the real world were modeled at the Geodatabase by the classes of relationship. Topology rules are defined To find errors in input data and avoid false edit data in future. At first, the ways of the region based on the type and length were classified into three groups of the alley, side street and main street. Then in urban thoroughfares Layer Descriptor tables, fields of ways streets vulnerability with fields of name, ID, commute time, unilateral and bilateral street, classification and length of streets and the relevant descriptive information have been filled. Results The results showed that according to the old texture Area 5 of Region 1 Kerman and low indexes of urbanization in the area, poor condition of buildings in terms of quality and date of their construction, high density constructions and residential buildings, the vulnerability of the network in the area is high. The map also indicates, due to high density building and being too vulnerable infrastructure in the area, vulnerability is about 90 percent of the ways in zone 1. As well as vulnerabilities is more in old parts and less width roads and ways. On the contrary, in regions 2,3 and 4 on account the relatively new buildings and more appropriate urban indicators, wider roads network and relatively more open spaces, the streets network has less than 35 per cent vulnerability. This highlights implies the need to revise laws and regulations to determine the density and maximum building height (by taking the width of the adjacent street) and the need to emphasis on the quality of urban construction than ever before. Determining the relative importance (weight) criteria by AHP hierarchical process showed that proximity to vulnerable equipment, such as a gas station or gas plant and the width of roads and ways have utmost importance. Criteria Slope and curvature streets and kind of using of buildings and the number of crossing roads and ways have the least important factors to determine the level of roads vulnerability. It can be concluded that the vulnerability of urban roads and paths in approach of earthquake disaster,  do not caused by a particular vulnerability criteria but the outcome of the factors and criteria that together provide vulnerability analysis paths.},  
Keywords = {City Crossovers Network, Vulnerability, Geometric Network, Crossover Network of the City, Crisis Management},
volume = {6},
Number = {4}, 
pages = {87-106}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-561-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-561-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Yousefi, H. and Javadzadeh, Z. and Noorollahi, Y.},  
title = {Industrial Waste Disposal Site Selection using Fuzzy-AHP Model in Salafchegan Special Economic Zone}, 
abstract ={Growth and development of the city and the urbanization, always has been accompanied with the development and human activities. On one hand, this companionship and coexistence has created the possibility of occupation and  economic capabilities for citizens, but on the other hand, they expose environment to pollution of air, water, soil, noise and visual and chemical pollutions. One of the environmental problems of the country is management of wastes especially industrial wastes. Among the important issues in the overall approach to comprehensive management of industrial wastes is the necessity of proper site selection for disposal. Considering the effects that waste disposal sites exert on the ecosystem and their surroundings, it should be noted that the site of disposal should be located where it develops the minimum destructive effects and inappropriate impact on their surroundings. The purpose of this study is the industrial waste site selection in the special economic zone of Salafcheghan. This area, as the most important and nearest special economic zone to the economical-political center of the country, with unique and privileged position to production, export and transit of goods, can play an important role in the macroeconomic of the country. Different parameters are effective on the industrial waste site selection. In this research, parameters such as groundwater depth, distance from surface water and groundwater, access ways, residential areas, Industries, power transmission lines, flooding, faults, slope and distance from the orchards and agricultural lands were studied.  In recent years, Arc GIS software has developed an undeniable transformation in the arena of study on geographical resources. Application of GIS science and technology in the studies related to industrial waste disposal sites can highly accelerate the preparation and combination of different data layers in the form of different conceptual models. Given the type of combination theory, in these models the number of data layers and value of each layer in the combination will be different. Some of these models are such as Boolean logic model, weights of evidence, index overlay, fuzzy logic, and weighted linear combination.  The combination of fuzzy logic and AHP method was used for site selection of industrial waste disposal, so this operation caused to increase of model's accuracy and reliability of results.  Following selection and preparation of the maps related to the influential parameters, giving weights was done through AHP and using expert comments. according to the effective factors and their AHP weights, maps of membership functions of each factor were prepared and fuzzy overlay was conducted by using And operator.  Thus, completely unsuitable to completely suitable areas were determined for industrial waste disposal in Salafchegan district of Qom province. These results also using satellite images were verified. Finally, the suitable areas in  the developement area of special economic zone of Salafcheghan are recommended to the relevant managers for disposal of industrial waste, because of reduction of transportation costs and exemption from customs payments law for wastes of foreign raw materials. },  
Keywords = {Site Selection, Industrial Waste, Geographical Information System (GIS), Fuzzy Logic, Analytical Hierarchy Process (AHP)},
volume = {6},
Number = {4}, 
pages = {107-121}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-558-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-558-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Farnaghi, M. and Rahimi, H.},  
title = {Spatio-Temporal Prediction of Monthly Rainfall using Deep Neural Network: A Case Study in North-west Iran}, 
abstract ={In today’s world, rapid and sustainable development is on the top of all countries agenda, including Iran. The major obstacles of sustainable development are climate and environmental conditions. One of the most important climatic constraint in Iran is insufficient rainfall. In addition to world water restrictions, Iran has approximately one third of the average global precipitation. Also the spatial distribution of rainfall due to natural conditions is very heterogeneous and temporal distribution of rainfall as well as the spatial distribution shows a similar trend. For these reasons, the water crisis has become a national predicament. In addition Floods and Droughts are the two faces of the same coin. Over the last few years, the overwhelming majority of disasters have been caused by floods in Iran. So Iran is amongst the few countries that is facing floods and drought simultaneously. Hence, long-term meteorological forecasting is of prime importance and plays a significant role in water resource management and sustainable development. This study presents an approach to forecast the monthly rainfall of north-western part of Iran and produce the spatio-temporal prediction maps in the study area. In this research, precipitation data along with environmental and meteorological information such as minimum monthly temperature, maximum monthly temperature, average monthly temperature, maximum wind speed and mean monthly wind speed from 1950 to 2014 were considered as affecting input parameters. Additionally, topographic parameters, elevation, latitude and longitude were computed from Digital Elevation Mode (DEM). In order to increase the prediction accuracy, large-scale climate data such as North Atlantic Oscillation (NAO), Antarctic Oscillation (AO), Extreme Eastern Tropical Pacific SST (Nino 1+2), Eastern Tropical Pacific SST (Nino 3), Central Tropical Pacific SST (Nino 4) and East Central Tropical Pacific SST (Nino 3.4), is used along with other environmental and topographical data. Considering the diversity of sources along with amount of the input data, we were facing challenges involving big data storage and processing. Hence for data storage, Cassandra as a NoSQL database was used. The two  main reasons to choose a No SQL database are robust and reliable architecture and providing a mechanism for storage and retrieval of data. Then, shallow artificial neural network and deep belief network as two branches of machine learning were trained and tested. Forecasted precipitation maps for twelve’s months of the 2014 were produced afterward using both shallow neural network and deep belief network. In order to evaluate and compare the performances of the networks, four criteria, including accuracy, precision, recall and f1 score, were used. The comparison between monthly precipitation forecast and measured precipitation shows that deep belief network is capable of handling very large spatial-temporal data sets and is also able to solve the complexities of forecasting precipitation. The results indicate that the accuracy of shallow and deep neural networks were 0.67 and 0.71, the precision were 0.69 and 0.69, the recall were 0.7 and 0.8 and the f1 score were 0.69 and 0.74, respectively.},  
Keywords = {Monthly Rainfall Prediction, Geospatial Information System, Big Data, Spatio-Temporal Distribution, Shallow Neural Network, Deep Neural Network},
volume = {6},
Number = {4}, 
pages = {123-142}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-555-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-555-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Behnabian, B. and MashhadiHossainali, M.},  
title = {Anisotropic Covariance Model Applied for the Computation of Surface Deformation using Least Squares Collocation, Case Study: Kenai Peninsula}, 
abstract ={Least squares collocation (LSC) is one of the usual methods for the interpolation of the GPS displacement field and computation of the surface deformations. The goal of this research is to improve the quality of prediction by LSC using anisotropic covariance model in which the covariance depends on azimuth and the distance of points. The Restricted Maximum Likelihood method (REML) is used for the precise determination of the parameters of anisotropic covariance model. For implementation of the REML method we have applied the Fisher scoring technique. The required mathematical relations for computation of the elements of strain tensor with LSC using anisotropic covariance model are also derived. We interpolated the displacement vectors on a regular grid of points with 15 minutes spacing in Kenai Peninsula in South central Alaska by the LSC method using the anisotropic covariance function. We employed a trend, signal noise LSC model in which de-trended data are used for estimation of covariance parameters. The input data is displacement vectors of a GPS network derived by processing the two campaigns of GPS observations in 1996 and 1998. The east-west and north-south components of the displacement vector were predicted independently from each other. In addition, the displacement vectors were interpolated employing an isotropic covariance function. In order to estimate the isotropic covariance parameters, we applied the method of model fitting to empirical covariances. The LSC computed results using the proposed method are compared with the isotropic covariance model. For this purpose, we applied the usual measures for precision and accuracy assessment of LSC predictions which are LSC cross validation errors and LSC prediction errors, respectively. Employing the proposed method, the root mean square of cross validation errors for east-west and north-south components of the displacement vector are reduced about 42% and 45%, respectively in comparison with the method employing isotropic covariance model. Meanwhile, the root mean square of LSC prediction errors reduced about 25% and 10% for east-west and north-south components. The strain tensor is also computed for the regular grid of points using anisotropic covariance model and the respective formula developed in this work. Principle strain components and dilatation parameter were computed from the estimated tensors and the results are illustrated graphically. Then, the pattern of deformation derived by the proposed method is compared with the known pattern of deformation in the region which is represented by other researchers. The figure of principle strains computed on a grid shows two components of contraction from north-west and south-east directions in the area in which the maximum computed contraction is located at the center of the northern area of Kenai Peninsula. Map of computed dilatation in this area also shows a dome type pattern of deformation in the region in which the maximum compression (negative dilatation) occurs at the same point in the middle of Peninsula. This is in agreement with the results derived from the leveling observations in the region which showed the maximum uplift at the same point. Shape of uplift in the region is also like a dome. Therefore, the proposed method and developed formulae were validated in two ways: One with the LSC prediction quality measures and the other by the independent leveling observations. Finally, we conclude that using the proposed method for this case study, one can get reliable LSC estimations even with the data points sparsely located in the region.},  
Keywords = {Anisotropic Covariance Model, Restricted Maximum Likelihood, Least Squares Collocation, Crustal Deformation},
volume = {6},
Number = {4}, 
pages = {143-159}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-552-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-552-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Habibpanah, A. and Amerian, Y.},  
title = {Regional Assimilation of International Reference Ionosphere Model using GPS Observations}, 
abstract ={The ionosphere layer of atmosphere environment is a highly variable media that performs significant weather variations with altitude, latitude, longitude, universal time, solar cycle, season, and geomagnetic activity. Therefore, ionosphere modeling and determining the total electron content (TEC) play an important role to know this layer of atmosphere specifications and control its effects on human activities. Different kinds of ionospheric models are widely used to monitor the changes in ionosphere in which single layer model (SLM) of ionosphere or TEC model has been always interesting for researchers. Complications of physical models for major of users, low accuracy of numerical (empirical) models for precise applications, the 24 hour delay in IGS daily global ionospheric map (GIM) propagation and the precise ionospheric information necessity in real-time and near real-time applications have been the reasons of development of new ionospheric models which is known as data assimilation models. These models combine measurements from observing system with the information obtained from background model trough the data assimilation technique. Assimilation algorithm involves a forecast step, in which a previous estimate of the state is evolved forward to the time of the observation, and an update or analysis step, where the evolved estimate of the state is updated using information from the observations. The outputs of assimilated models have parameters closer to the observations. The accuracy of the reconstructed ionosphere depends on the amount of assimilated data, the diversity of the data types and the quality of the data. Assimilated data may have different sources such as GPS slant TEC, in situ electron densities, electron density profiles (EDPs) from ground-based radars and ionosonde data in ionosphere data assimilation. In this study, precise TEC derived from dual frequency GPS observations are assimilated in to an international reference ionosphere (IRI) numerical model in analysis and forecast steps of assimilation. Kalman filter is used to increase the accuracy of IRI extracted TEC in analysis step and Gauss-Marcov Kalman filter (GM-KF) is used to predict TEC in forecast step for real-time and near real-time applications. Observations of 40 stations of Iranian permanent GPS Network (IPGN) in May 03, 2016 are used to extract precise VTEC for assimilation in IRI model. The GPS observed VTEC are compared with TEC form IRI model, TEC from IGS GIM and assimilated IRI TEC in analysis step of assimilation. The rote mean square (RMS) of discrepancy between GPS VTEC and IRI TEC are reduced from 9.8 TECU to 1.47 TECU at t=10UT, from 3.16 TECU to 0.98 TECU at t=14 UT and from 4.59 TECU to 1.39 TECU at t=18UT, after assimilation. Comparing the IGS GIM and assimilated IRI TEC with GPS observed VTEC indicate that the assimilated model is more accurate than GIM in Iran region. The GPS observed VTEC are also compared with TEC form IRI model, TEC from IGS GIM and assimilated IRI TEC in prediction step of assimilation. This comparison shows 90% improvement in assimilated TEC respect to IRI TEC at t=10 UT for Dt=0.5, 1 hour prediction time intervals. This improvement at t=14, 18 UT is more than 50% for Dt=0.5, 1, 2 hour prediction time intervals. By increasing the prediction time interval to Dt=5 hour, the assimilation accuracy tends to IRI model. Therefore the assimilated model has a good accuracy for real-time and near real-time applications.},  
Keywords = {Data Assimilation, IRI, GPS, Gauss-Markov Kalman Filter},
volume = {6},
Number = {4}, 
pages = {161-172}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-551-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-551-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Kabiri, K. and SaadiMesgari, M.},  
title = {Optimization of Pick up and Delivery Problem of Postal Service between the Centers by Capacitated Vehicles based on Metahuristic Algorithms}, 
abstract ={The development of effective decision support tools that can be adopted in the transportation industry is vital since it can lead to substantial cost reduction and efficient resource consumption. However, vehicles moving on our roads contribute to congestion, noise، pollution, and accidents. So route planning and transport management, using optimization tools, can help reduce transport costs by cutting mileage and improving driver and vehicle usage. In addition, it can improve customer service, cut carbon emissions, improve strategic decision making and reduce administration costs. Due to the simultaneous pick-up and delivery postal service and delivery time importance of those parcels, this study focuses on the pick-up and delivery problems. The pick-up and delivery problems are important types of vehicle routing problem (VRP). VRP is the core of scientific research on the distribution and transport of people and goods. Unlike the classical VRP, in which all customers require the same services, in the pick-up and delivery problem basic it is considered that two different types of services can be found in one place, in fact there's a pick up or delivery. PDP has several applications in the transportation of pick-up and delivery parcel post. The purpose of this research is to find the most optimal route to transport postal service. It is performed by imposing a series of conditions to the pick-up and delivery problems using meta-heuristic algorithms for the simulation data. It is followed by brief explanation of present metaheuristic algorithms including bee colony algorithm and genetic algorithms and their features. Finally the results of the algorithms are compared on the basis of the accuracy, repeatability, speed of convergence. It is necessary to note that the results are not ideal, but the best case is considered. The results showed the performance of the bee algorithm are better than genetic. Based on the results obtained in each run, genetic algorithms and Bee were 84% and 93% are possible to achieve the best solution.},  
Keywords = {Optimization, Pickup and Delivery Problem with Time Windows, Meta-Heuristic, Artificial bee Colony Algorithm , Genetic Algorithm},
volume = {6},
Number = {4}, 
pages = {173-184}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-443-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-443-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Fallahi, Gh. R. and Jamali, L.},  
title = {Landslide Hazard Zoning in the Siminehrood Catchment of Bookan Area by Combining Statistical Models, the Analytic Hierarchy Process and GIS}, 
abstract ={Landslide is a kind of natural disasters that annually causes great losses in life and property throughout the world. With the increasing population and expanding urban areas to the steep slope areas the risk of landslide has increased. The objective of this research is to study the risk of landslide in order to reduce potential damage and manage landslide risk. Siminehrood basin is the case study of the research. It is a subbasin of Orumiyeh lake catchement area at north west of Iran and due to its geographical location and natural characteristics is one of the country's landslide prone areas. In this research nine main factors affecting landslide including distance to fault, distance to road distance to stream network, topography, slope, aspect, landuse, precipitation and lithology are used. The weights of evidence model which is a bivariate statistical analysis and data-driven technique is used for producing Landslide susceptibility mapping. For obtaining the weights of  evidence  modeling  prior probability,  conditional  probability  and  the  positive  and  negative  weights  are calculated and a layer describing the total evidence weight for each class of factor layer is produced. Prior to combining the weights of factor layers, the importance weight of each factor layer is calculated using the Analytic Hierarchy Process (AHP). For implementation of the research the related spatial data were gathered and prepared in order to produce spatial layers for describing effective factors. For preparation of spatial data different processing have been performed such as distance tool for creating distance layer, interpolation tool for creation surface layer from point layer, hydrology tool set for extracting stream network from Digital Elevation Model (DEM) and classification tool. All of the preparation processes, calculation of importance of factor layers by using AHP method and overlaying of factor layers have been implemented in ArcGIS software package. A python application has been developed in ArcGIS environment in order to calculate the importance weight of each factor layer by using AHP. Finally, the landslide susceptibility map of Siminehrood Cachment was produced with overlaying the total evidence weight layers and applying the importance weight of each layer and then the resultant map was classified. For assessing the results of research the total occurred landslide data set in study area was randomly split into two training and testing landslide data sets. Calculation of success rate of landslide susceptibility map using testing landslide data sets indicated that about 60% of landslide pixels which has been used for producing classified landslide susceptibility map are in high risk class and the density of landslide pixels in this class is 37% while in the medium risk class is 8.8% and in the low risk class is 0.083%. Predicting rate of resultant map shows that about 56% of landslides which have been used for verifying and assessing the research results are in the high risk class of classified landslide susceptibility map.},  
Keywords = {Landslide, bivariate statistical analysis, Analytic Hierarchy Process (AHP), Siminehrood Basin, GIS},
volume = {6},
Number = {4}, 
pages = {185-199}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-512-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-512-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Borhaninejad, S. and Hakimpour, F. and Hamzei, E.},  
title = {Tags Extraction from Spatial Documents by Search Engines}, 
abstract ={Nowadays the selective access to information on the Web is provided by search engines, but in the cases which the system includes spatial information the search task becomes more complex and requires special capabilities in the search engine system. The purpose of this study is to extract the information which lies in the GML documents also implementation and evaluation of this extracted information retrieval method in an integrated approach. Our proposed system consists of three components: crawler, database and user interface. 1- Crawler: The main innovation of this study is this component. Crawler is a piece of software that after receiving the initial feed enters into Web pages and open links on each page and enters into the pages of these links. The crawler repeats this for new pages until all pages are reviewed and there are no new pages.  The typical spatial search engines crawlers analyze and process the HTML documents and extract spatial information contained in these documents. In our proposed system, the crawler processes GML documents text instead of HTML documents, and extracts the spatial information from these documents. Crawler in this system has two main tasks: - Detection of GML documents among the documents with different formats. - Parsing of GML documents and extracting the spatial information   2-Database: database has two major tasks in this system: - Storing data which collected by crawlers - Information indexing 3-User Interface: this section provides interaction between user and system and users send their queries to the system through this interface In general, this system's search process is done in two phases: online and offline. Offline phase includes the crawler's searching and storing the information into the database. And the online phase includes user interface and ranking operation. All in all, in this study the following objectives discussed: 1- Extraction of spatial information which is embedded in Web documents: Spatial documents include spatially explicit information such as the coordinates of the feature or the type of feature that extracting this information improves the response rate of spatial queries in search engines. 2- Implementation and evaluation of an integrated spatial information retrieval approach.  We have implemented this system as a pilot system on an Application Server as a simulation of Web. Our system as a spatial search engine provided searching capability throughout the GML documents and thus an important step to improve the efficiency of search engines has been taken. Despite the fact that today's engineers and specialists in many fields need raw spatial data and looking for it on the World Wide Web, most of spatial search engines are based on map representation and less attention is paid to spatial data. There is a substantial volume of spatial documents and information on the Web, however, the extent of the Web has caused this huge volume of documents and information hard to find among other information.Our proposed system as a spatial search engine provides the possibility of searching throughout the GML documents and thus it improves the efficiency of spatial search engines. Since GML documents include explicit spatial information along with non-spatial information, the main advantage of this system compared to other spatial search engines is an integrated approach to spatial and non-spatial data.},  
Keywords = {Spatial Search Engine, Spatial Documents, Crawler, GML},
volume = {6},
Number = {4}, 
pages = {201-216}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-511-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-511-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Lotfi, M. and Ghanbari, H. and Arefi, H. and Bahroudi, A.},  
title = {Mapping Alteration Zones using Gaussian Mixture Model and Spectral Angle Mapper}, 
abstract ={Due to the extent of mineral deposits, identification and proper management of resources is very important. According to the advent of remote sensing and specially producing hyperspectral remote sensing data which can get abundant spectral information, using this data for detailed study is rapidly expanding. Launch of the EO-1 in November 2000 introduced hyperspectral sensing of the earth from space through the Hyperion system. Hyperion has a single telescope and two spectrometers in visible near-infrared (VNIR) and short-wave infrared (SWIR). These spectral bands could provide abundant information about many important earth-surface minerals. Therefore one of the main aim of the present study was to examine the feasibility of the EO-1 Hyperion data in discriminating and mapping alteration zones around porphyry copper deposits (PCDs). The study area is situated at the Central Iranian Volcano-Sedimentary Complex, where the large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The visible near infrared and shortwave infrared (VNIR-SWIR) bands of data were used for image classifying and specially alteration mapping. The Pre-processing which was implemented on the level 1R Hyperion data in order to remove noise and acquire surface reflectance includes five steps that named removing uncalibrated bands, spatial displacement correction, destriping, spectral curvature (smile) correction and at last atmospheric correction. It is noticeable that atmospheric correction, because of using the target detection algorithm, SAM, is one of the most important step in this study. Therefore the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm, available in ENVI software, was implemented to obtain surface reflectance data. This algorithm is a MODTRAN4-based atmospheric correction software package designed to eliminate atmospheric effects through derivation of atmospheric properties such as surface albedo, surface altitude, water vapor column, aerosol, and cloud optical depths, as well as surface and atmospheric temperatures from hyperspectral data. In this paper Spectral Angle Mapper (SAM) and Gaussian Mixture Model (GMM) were implemented on pre-processed and calibrated Hyperion dataset. For using SAM algorithm, introducing reference spectra is obligatory. Information extraction from a Hyperion data set involves several processes including extraction of scene spectral endmembers using an integration of MNF, pixel purity index (PPI), and n-dimensional visualizer approaches. Then the extracted spectra which characterized using spectral analysis procedure available at ENVI and visual inspection, were used as reference for subsequent processing by SAM algorithm. On the other hand Gaussian mixture model (GMM) has been successfully used for HSI classification. It has also proved beneficial for a variety of classification tasks, such as speech and speaker recognition, clustering, etc. For estimating the parameters of GMM, the Expectation-Maximization (EM) algorithm was used.  In order to compare and assess the accuracy of methods proposed in this study, a simulated data used to demonstrate the efficiency of algorithms which used in this study. Results revealed that Hyperion data prove to be powerful in discriminating and mapping various types of alteration zones while the data were subjected to adequate pre-processing. Overall accuracy and kappa coefficient for results of SAM and GMM are 82%, 0.75 and 80%, 0.71 respectively.},  
Keywords = {Alteration Minerals,Spectral Angle Mapper, Gaussian Mixture Model ,Classification,Hyperspectral },
volume = {6},
Number = {4}, 
pages = {217-229}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-499-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-499-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}

@article{ 
author = {Sargolzaei, A. and Vafaeinejad, A. R.},  
title = {Finding Shortest Path in a Network by Using Cuckoo Optimization Algorithm and GIS}, 
abstract ={Nowadays with the rapid rate of urban development and increasing volume of vehicles and traffic restrictions, routing in urban networks is not only necessary but essential. Management of such massive volume of data makes the need to for GIS with capabilities to conduct spatial data analysis inevitable. People often, when deciding to start a journey from one location to another, consider not only which route and means of transportation will save them time, but also which are the most inexpensive and cost effective. Hence, they outline the issue as a question in their mind, and based on the criteria, seek to find the optimal solution. The same behavior occurs in a different routing system. Finding the most optimal, efficient and shortest route is one of the key pillars in route finding for which finding the right solutions could lead to answering other questions on the issue. In fact, for a more in depth level of analysis, the answer to this question is essential; Finding the shortest path possible from a starting point or origin, to an ending point or destination. Metaheuristic algorithms are estimating algorithms, that are able to find optimal or almost optimal solutions in a reasonable time.  The showcased methodology in this research for solving the optimal route is recommended for the first time and is the Cuckoo Optimization Algorithm. The reason for choosing this algorithm, is the fact that it is a new method that provides appropriate solutions for different problems than other meta-heuristic algorithms. Route finding which is by nature a discrete problem, is managed by changes in binary version of this algorithm.  In setting up the first population, a controlled approach was used to prevent the creation of random populations, that only a few of them could create routes. In this method, population variables that are basically the same network points and situations of each cuckoo are not randomly selected. These variables are selected in a controlled system. Meaning, selection of each next node is from those that are connected to it. While implementation of the algorithm, cuckoo’s locations are converted to binary numbers, if a node exists in the route it will become 1 and if not 0. A Sigmoid Function is used in the migration phase of the Cuckoo. In this phase the new location of Cuckoo stands between the range of zero and one, and other locations are converted to zero and one. To test the recommended algorithm, three network are used; hypothetical, local and real networks. The result of running this algorithm in 2 hypothetical and local networks with 20 and 31 nodes was the same result of a deterministic algorithm. However, in a network, that was part of a real network and composed of 617 nodes and 995 arcs, it could indicate the optimal route slightly better than that of deterministic algorithm. The results showed that the algorithm is capable of routing in the network and with some changes on the structure of the network can be used on networks with large data.},  
Keywords = {Finding Shortest Route in Network, Geographical Information System (GIS), Cuckoo Optimization Algorithm, Binary Encoding, Controlled Population},
volume = {6},
Number = {4}, 
pages = {231-239}, 
publisher = {انجمن علمي مهندسي نقشه برداري و ژئوماتيک ايران},
url = {http://jgst.issgeac.ir/article-1-496-en.html},  
eprint = {http://jgst.issgeac.ir/article-1-496-en.pdf},  
journal = {Journal of Geomatics Science and Technology},  
issn = {2322-102X}, 
eissn = {}, 
year = {2017}  
}