JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2020 | Volume:9 | Issue:3 |Papers Count:16

Plant growth and biomass assessments are required in production and research. Such assessments are followed by major decisions (e. g., harvest timing) that channel resources and influence outcomes. In research, resources required to assess crop status affect other aspects of experimentation and, therefore, discovery. Destructive harvests are important because they influence treatment selection, replicate number and size, and the opportunity for true repeated measures. For indirect biomass estimation, remote sensing data are used to determine agriculture species biomass using multiple regression analysis or Radiation Use Efficiency (RUE) models. In agriculture, RUE or Light Use Efficiency (LUE) is defined as dry biomass produced per unit of solar absorbed radiation or Photosynthetic Active Radiation. The LUE model needs a time series of NDVI index. Here, the lack of a few satellite images may make this time series incomplete. To overcome this deficiency, the farmer provided digital images that can be replaced for the missing satellite pixels/images that were deployed. Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial-based sensors. This study demonstrated a reliable, fast, and cost-effective approach for estimating NDVI using digital camera images. High-resolution digital images were acquired in the wheat field, and automated image processing methods were developed to segment the wheat canopy from the soil background. Exponential models for aboveground total NDVI showed acceptable precision and accuracy. Canopy cover estimated with images from digital cameras was sufficiently well correlated with satellite NDVI. Here, using a regression model, the NDVI index was estimated from the digital photographs. This method is named Digital NDVI (DNDVI). To develop this method, the relationship between the vegetation fractions (VF) obtained from the digital photos and the NDVI calculated from the satellite image of the same location were examined. For calculation of DNDVI to be used in cloudy days, the farmer is asked to supply a few photos from different parts of the farm (the number of photos depends on the size of the farm). These photos will be sent to the server where the VF values and then the averaged DNDVI will be calculated. The uncertainty of the DNDVI model in estimating biomass was 0. 071 with relative RMSE of about 0. 14. Next, wheat biomass was calculated using DNDVI and LUE model. The results of LUE model (and in estimating biomass show a coefficient of determination (R2) 0. 62 with an RMSE of 238 (gm-2). In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring wheat NDVI and growth status.

Road detection and Extraction is one of the most important issues in photogrammetry, remote sensing and machine vision. A great deal of research has been done in this area based on multispectral images, which are mostly relatively good results. In this paper, a novel automated and hierarchical object-based method for detecting and extracting of roads is proposed. This research is based on the Mean Shift Segmentation Method and the HSI Color Model for Road Detection. Initially, the multispectral images were segmented and then NDVI and NDWI spectral indices were created. In addition, the segmented images were transformed to HSI color space. Then, primary road surfaces were detected by Hue, NDVI, and NDWI spectral indices. In addition, the centerlines of roads were extracted using Voronoi diagram-based technique. After extracting of centerlines of primary roads, dangle errors were removed with emphasis on the topological rules and the lengths of dangles. In order to evaluate the proposed method, the Moonah multi-spectral Image provided by the ISPRS was used. According to the evaluation results, the parameters of completeness, accuracy and quality of the proposed method are, on average, estimated to be 98%, 84% and 84%. In addition, the results of the proposed method were compared with the results of five state of-the-art methods. The results demonstrate the high capability of the proposed method in detecting and extracting roads from satellite multispectral images in urban areas.

acquisition field reference data using conventional methods due to limited and time-consuming data from a single tree in recent years, to generate reference data for forest studies using terrestrial laser scanner data, aerial laser scanner data, radar and Optics has become commonplace, and complete, accurate 3D data from a single tree or reference trees can be recorded. The detection and identification of tree species and their precise spatial information are essential for the management of natural or man-made forests, and urban vegetation covers. Terrestrial laser scanners are active remote sensing sensors that offer the ability for generating high-level spatial information for forestry and nature conservation applications. A terrestrial laser scanner acquire detailed tree structure even in the sub-branch level. Hence, geometric information of the trees can be obtained with high accuracy from the terrestrial laser scanner point cloud data. The proposed process in this paper is to first use the laser data points of the terrestrial laser scanner of three different tree species: Quercus_petraea oak tree, Pinus_massoniana pine tree and Erythrophleum bean tree. geometric parameters of these trees These include extracted tree height, base canopy height, canopy height, canopy volume and tree diameter profiles. For each species, there were 12 single tree point cloud data of terrestrial laser scanner that were processed by the reference paper provider and the leaves of the trees were considered as noise and deleted. After the geometrical parameters of these trees have been extracted, considering these geometrical parameters (9 geometrical parameters) as a feature and using support vector machine algorithms and nearest neighbor classification of these three tree species done. It is worth noting that the accuracy of the methods for extracting the geometric parameters of trees has been evaluated by reference data that were produced non-automatically. In classification algorithm support vector machine is implemented in MATLAB programming language and RBF kernel is used for separation of three species and from each 12 point clouds of each species 8 point clouds as training data and 4 point clouds as test data are considered. In classifying the nearest neighbor, the value of K is empirically set when the algorithm is most accurate, and same as the SVM method of the 12 clouds available, 8 clouds are considered as training data and the rest of the clouds as test. One of the prominent goals of this study is to investigate the potential of the SVM and KNN for classificaction of tree species using few geometric features and few training samples. The evaluation results indicate the acceptable achieved accuracy 81% for the SVM algorithm and 74% for the KNN algorithm. In both SVM and KNN methods the accuracy of Q. petraea is 100% because the geometrical and structural features of this species are quite different from the other two species, which is clearly visualized in the images and the difference between the two The other class is completely done. The challenge of this classification relates to the other two species because they have almost identical geometrical parameters. The classification results show that the support vector machine algorithm with less training data performs better than the nearest neighbor algorithm in separating these two tree species.

The global burden of leptospirosis as a fatal zoonotic disease is increasing all over the world [1]. As there is not any significant decrease in yearly reported cases trend in Iran and potential spatial distribution of leptospirosis remain unknown in national level, we tried to figure out the geographic distribution pattern of leptospirosis in all parts of Iran. The aim of this study is producing leptospirosis risk map by analyzing relations between disease data reported by the Ministry of Health and nine environmental factors, for a period of 2009 to 2018, using Geospatial Information System (GIS) and Remote Sensing (RS) capabilities and Maximum Entropy (MAXENT) model. Altitude, precipitation, average temperature, maximum temperature, Normalized Difference Vegetation Index (NDVI), land cover, displacement (roads, railways and border entrance points), slope and water areas with 1km * 1km resolution were entered to the model as contributing factors, and patients home locations were used as disease incidence points. ArcGIS 10. 6. 1 and ENVI 5. 3 were used to prepare the nine factors for analysis and interpretation of the results. To create the potential distribution, MAXENT as an ecological niche model was used which is a method that its performance in disease distribution modelling has been proved [2, 3]. An advantage of this model is that variables can be either continuous or categorical and can be run for even less than 100 points as incidence data [2]. In this study, 60 percent of disease data was selected randomly for training and other 40 percent was applied as test data. Jackknife manipulation technique was performed to investigate the contribution of each variable in model. Our findings on spatial pattern of leptospirosis at least hint that except north parts of Iran that obviously are most vulnerable areas to the leptospirosis outbreaks, west parts of Iran specially Kermanshah are not safe from the spread of the disease, so health policy makers should consider these areas for monitor and control programs specially after severe rainfall or flood in spring and summer. Jackknife results showed that precipitation and altitude by 43. 5 and 37 percent contribution, are the two major factors for risk prediction of leptospirosis. On other hand, maximum temperature, water areas and slope have not meaningful impact on incidence of leptospirosis. Land cover with 11. 9%, NDVI with 4%, average temperature with 1. 3% and displacement with 1. 1% were participated in the model. Also, yearly models have been created for years between 2009 to 2018 to investigate that how parameters contributions change over years. Results showed that the incidence rate was related to altitude around 40% for all these ten years, but precipitation contribution percentage is fluctuating over years. Response curves showed a direct relation between incidence rate of disease and precipitation which means more rainfall causes more incidence. It also showed that altitudes around zero are the most suitable height condition on current distribution of leptospirosis. Also, the landcover output curve showed that Post-flooding or irrigated croplands, artificial surfaces and associated areas, mosaic forests or shrublands and grasslands are the most suitable landcovers for incidence of leptospirosis. To assess the model efficiency, Receiver Operating Characteristic (ROC) was employed. The Area under the Receiver Operating Characteristic Curve (AUC) for training data and test data was 0. 956 and 0. 955, respectively.

Point cloud and LiDAR Filtering is removing non-ground features from digital surface model (DSM) and reaching the bare earth and DTM extraction. Various methods have been proposed by different researchers to distinguish between ground and non-ground in points cloud and LiDAR data. Most fully automated methods have a common disadvantage, and they are only effective for a particular type of surface. Also, most of these algorithms have good outcomes in simple landscapes and not suitable in complex scene. In this article, the filtering methods are divided into three groups: First: traditional methods including slope-based methods, surface-based methods, morphology methods, TIN-based method, segmentation methods and other rule based filtering methods, second: methods that have specific algorithms or improved efficiency of existing algorithms and finally third filtering techniques: based on new machine learning and deep learning techniques. Then investigate and analysis comprehensively the operational problems, their challenges and efficiency of this methods for different areas mountain, forest, urban. Identify and advantages and disadvantages of each method and suggestions for using different methods in different areas is presented. The results of this analysis indicate that the combination of improved and new methods of machine learning and deep learning are suggested in order to improve the performance of filtering techniques.

Urmia lake due to the presence of various species of wildlife, species of vegetation on the islands, create a natural balance in the Azerbaijan region, tourist, recreational and social value, medical value, reserve of the Bio sepehr and as well as a wetland of international importance is special. Over the last few decades, use remote sensing technology to detect trends such changes various researchers have drawn attention to themselves. Factors that have caused Urmia lake will be in such a situation is varied. But in general, they can be divided into two categories: The factors that played a role in humans includes free use of water resources, agriculture unbridled development around the lake, and environmental factors like climate change, which according to the reduction of heavens and evaporation of Urmia lake water And reducing the flow volume and reduce annual temperature the lake ecosystem has been affected. Study of meteorological parameters of Urmia lake and investigation of its level changes in order to apply water resources management is important. Recent studies show which level and volume of lake water relatively decreasing. Urmia lake water level from 1992 to 1997 significantly increased and decreased from 1997 to 2009 and has remained almost constant since 2010. As a result, to rebuild the lake and managing the water resources of this lake is necessary, the role of effective parameters is determined. Therefore, neural network method was used in this research, meteorological parameters such as evaporation, temperature, precipitation, and annual amounts of groundwater abstraction of wells around the Urmia lake and the amount of water entering the lake, between 1997 and 2011, as input parameters And the annual altitude and area of the lake water entered the neural network as output parameters. In this research, the Levenberg rules were used to train the network. After training model by meteorological parameters, it was determined that the neural network model approximates the data in a perfectly accurate and accurate manner. It can also be predicted that changes in height and area occur by changing each of the parameters. This network estimates the lake area of Urmia at 3% error and 97% accuracy and lake level of 0/8 m. The correlation coefficient of the removal was obtained with the height and the range of-0. 4. The correlation coefficient of precipitation with 2 dependent parameters was obtained +0. 15 Input flow rate of +0. 4. After reviewing the model, it was found that the removal parameter from underground wells and the Input water volume into the lake compared to other parameters have a more significant effect on altitude and area. The results indicate that water use for agriculture and harvesting of water resources have increased And also the crops that are grown are products with a high water consumption pattern And also the water stored behind the dams has reduced the inflow to the lake.

Municipal solid waste collection is expensive and, in some cities, 46– 85% of their whole waste management expenses are used for waste collection and transportation. Rapid urbanization and every day human actions generate a large amount of waste from residential, commercial, or industrial extents all over the world. Waste collection optimization can decrease the waste collection budget and environmental emissions by reducing the collection route distance. Therefore, suitable planning of waste collection process can prevent additional costs and increase the efficiency of waste management. This paper aims to find appropriate routes for municipal waste collection using geospatial data to minimize total travel time of vehicles. On the other hand, the constraint set for the maximum travel time, the maximum total distance traveled, and the maximum number of checked waste bins have to be taken into account. In this research, the integration of the Vehicle Routing Problem (VRP) with the Geographic Information System (GIS) is used. Research scenarios are considered based on a fixed non-uniform fleet with a constant number of each types of vehicles in the Asymmetric Capacitated Vehicle Routing Problem with Backhauls and with Time Windows (ACVRPTW). The reason for choosing the ACVRPTW for this research is that in urban traffic network, vehicles have limited capacity, also urban passages are oriented asymmetrically, on the other hand, the time of service to buckets garbage is important and they should be viewed in a time window. In order to implement the proposed method, first, location data of waste bins and the average amount of waste generated per day are prepared. Also, the standards and policies of the waste management organization including the number and types of vehicles with their characteristics such as capacity and fuel consumption should be provided. The amount of waste generated by each trash should be calculated further. These data provide a pattern for the amount of waste accumulated in each garbage bin. The next step is solving the routing problem; in fact, the same VRP is executed in a specific way with the restrictions, parameters, and target function of the ACVRPTW. Then, the evaluation of the results will be accomplished. ACVRPTW was surveyed in a case study of Tehran, Iran. The results of the ACVRPTW are compared with real applications, indicating a decrease of 14% and 24% in each trip and whole travel time. In addition, the results also indicate that this research clearly contains a scientific approach to urban waste collection systems, and the proposed algorithm is able to take into account the constraints that the waste management organization has put forward. Furthermore, it determined the optimal time routes for each vehicle according to its characteristics. In fact, with this method, a network can be designed in order to reduce waste collection costs significantly. In other words, the proposed algorithm has been able to find an appropriate balance between the numbers of examined waste bins, the amount of collected waste, the mileage, and the duration time that each vehicle is serviced. It should be noted that the proposed algorithm runs in 5 minutes on average.

The quality of urban life considers as a key concept for meeting the basic needs of citizens in the context of general welfare, social well-being and people's satisfaction, as well as an effective tool for evaluating public policies, ranking places and monitoring urban planning and management policies and strategies. For this purpose, urban managers need to plan a standard environment for citizens using the studied policies from urban developers which leads to a better life quality. These studies are different depending on what kind of approaches and the quality assessment methods were used. Therefore, different approaches were used to assess the urban quality life, however, there were no extensive study to consider physical, spatial and social indicators. Many researchers believe that the life quality is a multi-dimensional concept and could be expressed using objective and subjective approaches. Therefore, the main objective of this research is to measure the quality of urban life based on two objective and subjective approaches at districts level in region one of Qom city, Iran. In this way, based on the study area features and available data, two domains of accessibility and sound pollution accompany with their indicators were assessed. These lasts were done by calculating the quantitative data, performing the qualitative analysis and questioning the citizens. In fact, there is a direct relationship between life quality and district features. If these features could be spatially optimized, their access will be simplified and ultimately will have positive effect on districts' residents and their life quality will be improved. On the other hand, the factors causing sound pollution such as vehicles, crowdsourcing on the street, day-to-day construction activities, increasing industries in the vicinity of cities are reducing the quality of life. In this research, for accessibility domain, indicators like administrative, educational, commercial, health-therapeutic, sport-recreational, cultural-religious and green space were considered and for sound pollution domain, street network, urban land use and population density were considered. For extracting and modeling these ten indicators, in two principal domains of this research, the three layers of land use, street network and population were used. By producing classified maps of two domains of accessibility and sound pollution in objective and subjective dimensions, the correlation between objective and subjective outcomes was investigated. In addition, neighborhoods ranking in terms of urban life quality was assessed. The results of integrating these layers, with regard of objective approach, showed a particular pattern of life quality rate. This pattern demonstrated the highest life quality rate in city center and it decreases gradually when we distance from city center. However, the spatial analysis of statistical data showed different pattern. Finally, in order to provide a management tool, neighborhoods were ranked based on the final indicators extracted from TOPSIS method. The best sub-districts, based on objective and subjective approaches, were Nobahar and Bajak-3. One of the other objective of the study was to investigate the correlation between the results of life quality indicators in two objective and subjective dimensions. So they were analyzed separately using Pearson's correlation coefficient. The results showed a high correlation between objective and subjective results in commercial and green space indicators, and a significant positive relationship was observed in street network, administrative, cultural-religious, health-therapeutic and population density indicators, however, the other indicators demonstrated an inverse correlation. Overall, it can be concluded that the subjective results are more reliable than the objective results.

Aboveground biomass (AGB) of forests is an essential component of the global carbon cycle. Mapping above-ground biomass is important for estimating CO2 emissions, and planning and monitoring of forests and ecosystem productivity. Remote sensing provides wide observations to monitor forest coverage, the Landsat 8 mission provides valuable opportunities for quantifying the distribution of above-ground biomass at moderate spatial resolution across the globe. The combination of the sample plots and image data has been widely used to map forest above-ground biomass at local, regional, national, and global scales. Many predictive methods have been suggested to estimate forest aboveground biomass from sparse sampling points into continuous surfaces, ranging from regression methods such as Geographically Weighted Regression (GWR) and geostatistical methods such as Regression Kriging (RK). Researchers have been particularly interested in understanding the causes and effects in ecosystem functions of spatial autocorrelation and heterogeneity, over the past decade. Where in forestry data include spatial autocorrelation and heterogeneity, the independence and homogeneity assumptions of standard statistical approaches, such as ordinary least squares (OLS), may be violated. Many spatial models (such as Geographically Weighted Regression and Regression Kriging) have been developed in recent years to discuss spatial effects in the relationships between variables. Spatial models can be divided into global and local models depending on the spatial scales used in the modeling process. A global model usually involves, a tool to model spatial autocorrelation between observations in neighboring locations, through either a covariance matrix that can be calculated using a variogram or spatial weight matrix based on neighborhood proximity. Global models, of course, do not well represent spatial differences at any given location and may not be successful in dealing with spatial heterogeneity. By comparison, local models, such as geographically weighted regression, adequate a regression relationship within a given bandwidth for each spatial location using the neighbors. From the relationships between variables, the local models are more useful in exploring locational spatial variation (heterogeneity). In the present study, using a Landsat 8-OLI image, and Geographically Weighted Regression and Regression Kriging modeling were compared for the estimation of aboveground forest biomass. In this study, we gathered aboveground biomass data from a total of 184 (30 × 30 m) sample plots in Zagros forests in the Kohgiluyeh and Boyer-Ahmad Province. The datasets corresponded to the Landsat 8 image pixel values. We applied the species-specific allometric equations for individual trees to estimate forest aboveground biomass. The aboveground biomass at plot-level is simply the summation for all trees within the same plot. The estimates were evaluated by ten-fold cross-validation and performances of the model was evaluated using the coefficient of determination (R2) and relative root mean squared error (RMSE%). The efficiency of the predictions can be described with the scatterplots showing the relationships between the forest above-ground biomass estimates and reference data. Results showed 1) that Geographically Weighted Regression (R2 = 0. 61, RMSE%= 22) was a fairly better approach and could provide promising results for the prediction of forest above-ground biomass compared to Regression Kriging (R2 = 0. 47, RMSE%= 28) and 2) scatterplots depicted that the problems of overestimation and underestimation for all the prediction were apparent.

The purpose of this paper is to evaluate and improve the accuracy of indoor positioning using smartphone sensors based on Pedestrian Dead Reckoning (PDR) method. In some specific situations, such as fires or power outages that disable infrastructure-based positioning techniques, using PDR method based on smartphone sensors that perform positioning continuously is a good solution. This paper focuses on determination of the user’ s movement type to evaluate effective components of indoor positioning method. First, movement samples are evaluated with the feature-vectors of data from sensors and three classification algorithms (Decision Trees (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN)). From the perspective of feature-vectors, the proposed features significantly improve the performance of three classification algorithms compared to previous research features. From the perspective of classification algorithm also Support Vector Machine had best performance with %99. 3 accuracy, while spending the most time. In the second phase, step detection is performed for norm acceleration values based on the definition of the upper and lower threshold and the time threshold. The directional component is also obtained by combining accelerometers, magnetometer and gyroscope sensors. Localization tests were performed while the user holding the phone in front of him with two states (normal walking, running) in three paths of different geometry (squares, circles and rectangles). The final accuracy obtained from normal walking test for three paths of square, circular and rectangular shapes was %4. 8, %3. 6, and %2, respectively. The final accuracy of the running mode was also obtained for three paths of square, circular and rectangular shapes equal to %8. 4, %5. 7, and %4, respectively.

Urban transportation is one of the most important issues of urban life especially in big cities. Urban development, and subsequently the increase of routes and communications, make the role of transportation science more pronounced. The shortest path problem in a network is one of the most basic network analysis issues. In fact, finding answers to this question is necessity for higher level analysis. In general, shortest path solution methods using optimization algorithms are divided into two categories: exact and approximate algorithms. In exact algorithms, achieving the optimal solution requires time, and consequently more cost. On the opposite side, there are some approximate algorithms that work in a short period of time. Meta-heuristic algorithms are among approximate algorithms that are capable of finding optimal or near-optimal solutions in a reasonable period of time. The method used in this study is to solve the shortest path problem with the combination of Genetic meta-heuristic (GA) and Tabu Search (TS) algorithms. GA is inspired by genetic science and Darwin's theory of evolution; it is based on survival of the highest or natural selection. A common use of genetic algorithms is to be used as an optimization function. In GA, the genetic evolution of living things of life is simulated. Inspired by the evolutionary process of nature, these algorithms solve problems. GA forms a set of population (solutions), then it achieves an optimal set by acting some possess on the correct set. To solve a problem by genetic algorithms, it is necessary the problem is converted to the specific form required by GA. On the other hand, TS algorithm is not population-based. It obtains an answer, then it tries to direct the answer to the optimal solution by applying a series of operators. This algorithm is highly similar to the Simulated Annealing algorithm. In this paper, for solving the shortest path problem, a series of geometric pre-processing on the network is done to generate a search area around the source and destination nodes. In the proposed algorithm, the cost function is defined as a complex number, which the real part shows the sum of the weight of the real edges, and the imaginary part denotes the number of virtual edges. The innovation of this research is about applying Tabu Search algorithm in mutations process of genetic algorithm. The proposed method overcomes the inappropriate response of the pure genetic algorithm in terms of the final weight of the path especially the large networks. In order to evaluate the efficiency of the proposed algorithm, the algorithm was implemented on a real directional network which is part of Tehran city road networks including 739 nodes and 1160 edges. The results show that in the proposed algorithm, the length of the path is as close as possible to the solution obtained from the definitive Dijkstra’ s algorithm. This algorithm predicts approximately the final path length of 5% more than Dijkstra’ s algorithm. But in terms of running speed, it is 5. 12 times faster than the Dijkstra’ s algorithm. In comparison with the pure genetic algorithm, the proposed algorithm is 9% shorter in average in terms of path length. And about the running time, the speed of the proposed algorithm is approximately equal to the pure genetic algorithm. Regarding to repeatability, the proposed algorithm also shows 25. 36% of repeatability.

Solar energy is an important source of renewable, sustainable, and accessible kind of energy in the world. Halting environmental degradation, this kind of energy is what can reduce the damaging consequences of fossil fuels. Iran or other counties located in the arid area can use this type of energy. In fact, the geographical position of Iran in word's dry belt makes this country a proper land for constructing facilities of using solar energy. One of the most usual facilities, which use solar energy are photovoltaic power plants. Photovoltaic power plants need special situations to get the best efficiency. First of all, they need the highest portion of sunny days in a year. Moreover, to reduce the cost of construction, they must be built near the roads and power transmission lines. To find the places which include the best conditions, GIS is a useful system. GIS is able to prepare maps of the desired conditions and then combine them using various approaches to find the best locations. Many researches have already been done around the world for site selection of photovoltaic power plants. There are some researches in Iran, as well. However, the criteria used in these researches seems not to be comprehensive enough for such a sensitive site selection. Thus, this research aims to take as much as possible criteria for site selection of photovoltaic power plants into account. The goal of this research is to find the potential area for constructing photovoltaic power plants in Iran. This purpose contains four main sections such as assessing effective factors on the operation of photovoltaic power plants, preparing factor maps (criteria maps) in GIS environment regarding the spatial nature of the effective factors, the maps conversion into fuzzy form and proper combination, and finally, classification of result into four levels. Using fuzzy logic helps us to consider unavoidable uncertainty that exists in spatial data. In this study, the prepared factor maps are first converted into a fuzzy form using fuzzy membership functions. Then, using fuzzy overlay, the maps are combined. To set the most strict situation, among the fuzzy logical operators, fuzzy AND was selected. Thus, we will be sure that the final selected sites will have the best possible conditions. The results indicate that six provinces, including Kerman, South Khorasan, Fars, Yazd, Hormozgan, and Sistan-o-Baluchestan, achieved the highest score for constructing photovoltaic power plants in Iran. Overlay, more than 557000 square kilometers of Iran have a high potential for gaining solar energy. The results also show that north and north-west of Iran in compare to the other areas, achieve less suitability for constructing photovoltaic power plants. The most important criterion that causes these situations is the portions of sunny days. While in the north side of Iran, as well as the mountainous areas such as Zagros Mountains, the precipitation is higher than the other regions, the number of sunny days in a year is less. This study detects the proper sites in a general form. The scale of the study covers the whole country. Thus, most fine-scale studies must be performed on the selected regions to find the best specific sites.

In this study, a review of indoor positioning methods based on indoor environment models is presented. Researchers introduce a variety of methods in this field. The purpose of this study is to present different methods and evaluate them. Each method is described from the point of view of the use of the spatial models. In the end, methods advantages and disadvantages have been introduced. Due to the wide variety of methods and Principles used in this field, the choice of an optimal method is not possible. Because each one has a different performance depending on the indoor environment and problem. It seems that, if it is possible, a combination of methods is an effective solution, because in some cases the advantages of a method cover the disadvantages of another method.

Landslide is one of these natural hazards which causes a great amount of financial and human damage annually allover the world. Accordingly, identification of areas with landslide threat for implementation of preventive measures in order to confront against the instability of hillsides for reduction of potential threats and related risks is very important. In this research a new method for classification of landslide risk according to geographical analysis and uncertainty modeling is presented which is based on data mining in previous events. In order to do so, adaptive neuro-fuzzy algorithm which is adjusted by means of sensitivity analysis is used in inferential basis of proposed model, which analyze landside risk efficiently. The selected region for this study is available lands in Alborz province. In proposed method factors like altitude, petrology, gradient, gradient direction, distance to fault and rainfall which are some of the most serious causes of hillside's instability had been inserted and their raster maps produced in GIS context and stored in georeference database. In the next step, areas prone to landslide had been identified according to findings of proposed model and finally in addition to model evaluation according to validation outputs, another round of validation is done by field monitoring of hih-risk regions and interpretation of provided 3D models. Results show that the proposed model with root mean square error of 0. 819 and correlation factor of 0. 934 has a relatively high accuracy in classification of landslide risk. In addition in landslide risk geographical distribution map inside studied region, the area of landslide-prone area is the highest with respect to total area of province which shows high-risk of Alborz province against landslides.

Recently, increasing population rate and urbanization growth, made the significance of land use more to double; So that the plan of land use in the cities has been encountered with vast imposed changes. In order to represent those changes, this study aims to model the land use changes, as an example in Khorramabad city, Lorstan province, Iran. In this regard, the raw Satellite images which captured by Landsat TM, ETM+ and OLI sensor images, corresponding to three decades of 1995, 2005 and 2015, were used. The city maps were then obtained through image processing including image geometric, omission and radiometric corrections and also implementing maximum likelihood classification methods on the images of the years studied. Then we investigated and compared different approaches for modeling, considering affecting parameters on urban development, as the simulation accuracy criteria, including: distance from the river, distance from the road, distance from the village, slope, direction, height and urban land-use in the base year. The different simulation approaches are including: the GEOMOD model, Sim Weight based learning algorithm and artificial neural network (MLP). GEOMOD selects the location of network cells according some rules: 1) resistance: simulating route of changes. 2) Regional categorization: simulating land use changes in a series of regions as the category. 3) Neighborhood instruction. 4) Providing a scale map: in the GEOMOD model, before implementation of the modeling process a scale map must be prepared; this map is used for simulating the change from one category to the other until the model imposes changes based on the map. For implementing the GEOMOD model, two images are required or even one image can be used, and instead of the second image, we can substitute the area extent of the considered land-use in the second image. The artificial neural network is a powerful tool for creating models, especially when the relationship between the infrastructural data is unknown or latent. In a multilayer perceptron artificial neural network model, the transform potential map is derived from implementation then based on Markov chain theory the model of future is estimated. SimWeight is a learning machine which is simpler than the multilayer perceptron neural network. The logic structure of Sim Weight performs based on the nearest neighbor algorithm with the difference that Euclidean distance from the specified samples of categories are weighted. After introducing the parameters affecting the LMC program (included in the terSet software), by implementing SimWeight algorithm, the weights are determined and assigned to each of parameters. At the prediction stage new weighted parameters and specify the future model using Markov chain algorithm. The necessity of using any kind of topic information is the knowledge of accuracy. Accuracy of information is in fact the probability of information accuracy. In executive projects which consider the comparison of accuracy, the Kappa index is often used as accuracy criteria, because it takes into account false classified pixels. In the present research, the simulation performed using the mentioned methods. Finally, for validation, the simulated maps and the real ground map was matched with each other. The results reveal the GEOMOD model, SimWeight based learning algorithm and MLP algorithm have the kappa coefficient of 0. 79, 0. 77 and 0. 72, respectively. Hence, the GEOMOD for the urban development simulation in Khorramabad has better performance than the other models. Therefore preferred GEOMOD model to predict urban development recommended to all managers, Municipal authorities and affair planners.

A Chat Bot is an automated operator that can interact with customers like a human operator, answer their questions, solve problems and get feedback. Real-time responsiveness, the sense of talking to a human user is one of their good features that can be used to deliver location-based services. This paper designed a Chat Bot that can talk and answer users' questions based on their location. This Chat Bot is a 24-hour smart system that Without fatigue, and the influence of environmental stimuli Provides service to users. Users who use this Chat Bot have 98% satisfaction compared to other available services. Conversational text chat capabilities, quick response, ease of use and a sense of human interaction are among the reasons for the satisfaction of the Chat Bot users.