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Oak decline is a complex phenomenon. The classification of oak decline potential could be a valuable tool for forest management. This paper identified seven factors that influence oak decline: height, slope, aspect, temperature, perception, soil type, and aerosol. Then, factor analysis is used to determine factors that should be included in oak decline potential classification and reduce data complexity.As a result, five components explaining 92.49% of total variance are selected. The first component explains 40.34% of the variance, and three factors, including perception with positive and temperature and aerosol with negative load, have contributed to its construction. The second component is composed of a positive load of aspect, and soil type explains 14.89% of the variance. By explaining 14.10% of the variance, the third component consists of soil type and aspect with positive and negative loads, respectively. Slop and height have a positive load in constructing the fourth and fifth components.Five extracted components are used as input sets of PNN, MLC and SVM methods. 80% of samples are used for training methods, and 20% are used for testing purposes. Results are compared based on the overall accuracy of the methods.These components are used as an input set of three classification methods, including Probabilistic Neural Network (PNN), Maximum Likelihood Classification (MLC) and a Support Vector Machine (SVM). Based on the results, the SVM, with an overall accuracy of 0.87%, has proved its capability in oak decline potential classification.

The large quantity of movement data collected from various sources can be inherently uncertain and heterogeneous. In the movement data analysis and mining spectrum, computing the similarity of trajectories while considering the uncertainty and heterogeneity has been less addressed. Generally, two factors of sampling and positioning error cause uncertainty in trajectory databases. Therefore, in this research, a method based on the longest common subsequence (LCSS), named FLCSS, is proposed that uses fuzzy theory and the bead model to consider the uncertainty of trajectories originated from positioning and sampling errors. The performance of FLCSS is evaluated by implementations on real and synthetic datasets, and compared with six important and commonly used similarity measurement methods, namely, LCSS, edit distance on real sequence (EDR), dynamic time warping (DTW), edit distance with real penalty (ERP), Hausdorff distance (HD), and Fréchet distance (FD). The results show that FLCSS has a better performance compared to other methods, in terms of sensitivity to point displacement, noise, and different sampling rates. Furthermore, the high correlation between FLCSS and LCSS (ρ = 0.91) confirms the robustness of the proposed method in considering uncertainty in the trajectory databases.

Noise pollution is one of the non-natural hazards in cities. Long-term exposure to this kind of pollution has severe destructive effects on human health, including mental illness, stress, anxiety, hormonal disorders, hypertension and therefore also cardiovascular disease. One of the primary sources of noise pollution in cities is transportation. The COVID-19 outbreak caused a significant change in the pattern of transportation in cities of Iran. In this article, we studied the spatial and temporal patterns of noise pollution levels in Tehran before and after the outbreak of this disease. An overall analysis from one year before until one year after the outbreak, which showed that noise pollution in residential areas of Tehran had increased by 7% over this period. In contrast, it had diminished by about 2% in the same period in the city centre and around Tehran’s Grand Bazaar. Apart from these changes, we observed no specific pattern in other city areas. However, a monthly data analysis based on the t-test, the results show that the early months of the virus outbreak were associated with a significant pollution reduction. However, this reduction in noise pollution was not sustained; instead a gradual increase in pollution occurred over the following months. In the months towards the end of the period analysed, noise pollution increased to a level even higher than before the outbreak. This increase can be attributed to the gradual reopening of businesses or people ignoring the prevailing conditions.

The learning similarity between users and points of interests (POIs) is an important function in location-based social networks (LBSN), which could primarily benefit multiple location-based services, especially in terms of POI recommendation. As one of the well-known recommender technologies, Collaborative Filtering (CF) has been employed to a great extent in literature, due to its simplicity and interpretability. However, it is facing a great challenge in generating accurate similarities between users or items, because of data sparsity. Traditional similarity measures which rely on explicit user feedback (e.g., rating) are not applicable for implicit feedback (e.g., check-ins). In this study, we propose multi-criteria user–user and POI–POI similarity measures, based on the dominance concept. In this regard, we incorporate geographical, temporal, social, preferential and textual criteria into the similarity measures of CF. We are interested in measuring POI similarity from a location perspective, by taking into account the influence of the dominance concept on multiple dimensions of POIs. To evaluate the effectiveness of our method, a series of experiments are conducted with a large-scale real dataset, collected from the Foursquare of two cities in terms of POI recommendation. Experimental results revealed that the proposed method significantly outperforms the existing state-of-the-art alternatives. A further experiment demonstrates the superiority of the proposed method in alleviating sparsity and handling the cold-start problem affecting POI recommendation.

Climate and land-cover change are critical drivers of avian species range shift. Thus, predicting avian species' response to the land and climate changes and identifying their future suitable habitats can help their conservation planning. The common pheasant (Phasianus colchicus) is a species of conservation concern in Iran and is included in the list of Iran’s protected avian species. The species faces multiple threats such as habitat destruction, land-cover change, and overhunting in the country. In this study, we model the potential impacts of these two on the distribution of common pheasant (Phasianus colchicus) along elevational gradients in Mazandaran province in Iran. We used Shared Socioeconomic Pathways (SSP) scenarios and the 2015–2020 trend to generate possible future land-cover projections for 2050. As for climate change projections, we used Representative Concentration Pathway (RCP) scenarios. Next, we applied current and future climate and land-cover projections to investigate how common pheasant’s habitat changes between 2020 and 2050 using Species Distribution Modeling (SDM). Our results show that the species has 6000 km2 suitable habitat; however, between 900 to 1965 km2 of its habitat may be reduced by 2050. Furthermore, we found that the severity of the effects of climate change and land-cover change varies at different altitudes. At low altitudes, the impact of changing land structure is superior. Instead, climate change has a critical role in habitat loss at higher altitudes and imposes a limiting role on the potential range shifts. Finally, this study demonstrates the vital role of land cover and climate change in better understanding the potential alterations in avian species' habitats.

The ever-increasing emergence of Aedes albopictus as one of the most significant vectors of arboviruses like Zika, chikungunya, and dengue requires deeper studies in new areas using environmental factors. Almost 2 billion people in tropical and subtropical zones are exposed to the vector. Because of the vector’s tendency to reproduce in a variety of habitats, including urban, suburban, and rural areas, the species is spreading rapidly wherever a set of climatic factors is available. Iran as a country of diverse climates and biomes with more than 80 million population is highly prone to the disease. Hence, this study aims to monitor the risk probability of the mosquito’s presence according to the environmental parameters in Iran. In this research, we classified each parameter based on the appropriate conditions for the breeding and activity of the vector. To calculate the weight of each parameter, we applied analytical hierarchy process (AHP) as an expert-based decision-making method and risk map generated using spatial analysis. Finally, to classify the values of the risk map and finding the most important risk areas, we performed a grouping analysis. The result showed that 5 coastal counties in Guilan province in the north and 6 counties in Khuzestan province in the southwest of Iran were ranked first and second riskiest places, respectively. Due to the semi-tropical climate of the coastal areas, they record a suitable pattern of contributing parameters for the presence of the vector. These findings help for the public health policymakers to control the invasion of Aedes albopictus to estimate the related disease occurrence.

Zoonotic cutaneous leishmaniasis (ZCL) is one of the most prevalent zoonoses in Iran, especially in central and northeastern Iran. This research aimed to examine whether there were spatiotemporal clusters of ZCL cases, and if so, whether there were differences in clustering according to age, sex, area of residence, and occupation. Spatial analysis, including global and local spatial autocorrelations, inverse distance weighting, and space-time scan statistics, were used to determine potential clusters in the villages of Golestan from 2011-2016. Several spatially significant (p < 0.05) clusters were observed in the north and northeastern regions, and most persisted until the last year of the study period. Children (0-10 years) living in rural settings were more likely to have an infection than those living in other areas. Although the disease was centered in the northern regions, housekeepers, females, and patients aged 21-30 and 41-50 years were found to be the high-risk groups in the southern areas. The seasonal pattern indicated that the outbreak mainly began in late summer, peaked in October, and diminished in December. By exploring spatiotemporal variations of ZCL by sociodemographic information, this study was able to identify priority areas for decision-makers in healthcare and resource allocation.

Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories’ simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems.

Maritime traffic prediction is a crucial task for increasing the efficiency of port operations and safety, especially in congested regions. A huge amount of automatic identification system (AIS) da...

Dynamic routing is an essential tool for today’s cities. Dynamic routing problems can be solved by modelling them as dynamic optimization problems (DOPs). DOPs can be solved using Swarm Intelligence and specially ant colony optimization (ACO) algorithms. Although different versions of ACO have already been presented for DOPs, there are still limitations in preventing stagnation and premature convergence and increasing convergence rate. To address these issues, we present an in-memory pheromone trail and an algorithm based on it (named AS-gamma) in the framework of ACO. In-memory pheromone trail is effectively increasing diversity after a change in an environment. Results of experimenting AS-gamma in three scenarios on a real-world transportation network with different simulated traffic conditions demonstrated the effectiveness of the presented in-memory pheromone trail method. The advantages of AS-gamma over three existing DOP algorithms have been illustrated in terms of solutions quality. Offline performance and accuracy measures indicate that AS-gamma faces less stagnation, premature convergence and it is suitable for crowded environments.

Land subsidence, as a dangerous environmental issue, causes serious damages to farms and urban infrastructure. In this regards, this research was conducted with aimed to assess the efficiency of hybrid algorithm Particle Swarm Optimization–Random forest (PSO-RF) for developing land subsidence prediction model. PSO algorithm was used to select the factors affecting land subsidence, and RF algorithm was used as a classifier. Initially, the subsidence map of the region was obtained using the SBAS-DInSAR method throughout, for 2004 to 2009. The subsidence pattern was V-shaped, with an average of 13.8 cm per year. Then 11 factors dependent to the land subsidence event were prepared as PSO-RF inputs in GIS environment. Then, the weight of each of these factors was calculated using frequency ratio. Finally, 8888 points were randomly extracted from the subsidence map that had effective factors in land subsidence, as well as class 0 (no subsidence) or 1 (subsidence). About 6255 samples were selected for training and 2633 samples for validation of the model. The accuracy of the generated maps was then evaluated using the area under the receiver operating characteristic curve (AUC), RMSE and the accuracy (AC). The PSO-RF approach had a strong predictive accuracy with the smallest prediction error to map the LS hazard subsidence (i.e., AUCtraining = 93.2%, AUCvalidation = 89.8%, ACtraining = 0.86, ACvalidation = 0.81, RMSEtraining = 0.43, RMSEvalidation = 0.55). It was found that the media aquifer was the furthermost effective factor in the land subsidence development and followed by groundwater drawdown and transmissivity and storage coefficient.

For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.

Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a critical role in identifying prone areas and reducing the damage caused by landslides in each area. In the present study, deep learning algorithms including convolutional neural network (CNN) and long short-term memory (LSTM) were used to identify landslide prone areas in Ardabil province, Iran. Equql to 312 landslide locations were identified and randomly divided into train and test datasets at 70–30% ratios. Then, according to previous studies and environmental conditions in the study area, twelve factors affecting the occurrence of landslides were selected, namely altitude, slope angle, slope aspect, topographic wetness index (TWI), profile curvature, plan curvature, land-use, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. The ratio of the importance of each influential factor in landslide occurrence was obtained through information gain ranking filter (IGRF) method and it was found that land-use and profile curvature had the highest and lowest impacts, respectively. Afterwards, LSMs were generated using CNN and LSTM algorithms. In the next step, the performance of the models was evaluated based on the area under curve (AUC) value of receiver operating characteristics curve and the root mean square error (RMSE) method. The AUC values for CNN and LSTM models were 0.821 and 0.832, respectively. Furthermore, the RMSE values in the CNN model for each of the training and testing dataset were 0.121 and 0.132, respectively. The RMSE values in the LSTM model for each of the training and testing dataset were 0.185 and 0.188, respectively. Therefore, it can be concluded that CNN performance is slightly better than LSTM; but in general, both models have close performance and the accuracy of both models is acceptable.

This study is dedicated to modeling the spatial variation in COVID-19 prevalence using the adaptive neuro-fuzzy inference system (ANFIS) when dealing with nonlinear relationships, especially useful for small areas or small sample size problems. We compiled a broad range of socio-demographic, environmental, and climatic factors along with potentially related urban land uses to predict COVID-19 prevalence in rural districts of the Golestan province northeast of Iran with a very high-case fatality ratio (9.06%) during the first year of the pandemic (2020–2021). We also compared the ANFIS and principal component analysis (PCA)-ANFIS methods for modeling COVID-19 prevalence in a geographical information system framework. Our results showed that combined with the PCA, the ANFIS accuracy significantly increased. The PCA-ANFIS model showed a superior performance (R2 (determination coefficient) = 0.615, MAE (mean absolute error) = 0.104, MSE (mean square error) = 0.020, and RMSE (root mean square error) = 0.139) than the ANFIS model (R2 = 0.543, MAE = 0.137, MSE = 0.034, and RMSE = 0.185). The sensitivity analysis of the ANFIS model indicated that migration rate, employment rate, the number of days with rainfall, and residential apartment units were the most contributing factors in predicting COVID-19 prevalence in the Golestan province. Our findings indicated the ability of the ANFIS model in dealing with nonlinear parameters, particularly for small sample sizes. Identifying the main factors in the spread of COVID-19 may provide useful insights for health policymakers to effectively mitigate the high prevalence of the disease.

This study compares two adaptive neuro-fuzzy inference system (ANFIS) and principal component analysis (PCA)-ANFIS techniques for spatial modeling and forecasting of zoonotic cutaneous leishmaniasis (ZCL) cases in rural districts of Golestan province, Iran. We collected and prepared data on ZCL cases and climatic, topographic, vegetation, and human population factors. By applying the PCA algorithm, the parameters affecting the ZCL incidence were decomposed into principal components (PCs), and their dimensions were reduced. Then, PCs were used to train the ANFIS model. To evaluate the proposed approaches in model assessment phase, we used test data in 2016. In this phase, we showed that PCA-ANFIS model with values ​​of R

The concept of environmental sustainable development is in fact a response to the environmental and social damaging effects. Urban sustainable development is one of the foundations for achieving environmental sustainable development and social justice; thus, the location allocation of urban facilities has to be optimized. Location allocation models are among the most widely used methods in GIS spatial analysis. Owing to their importance in recent decades, many unified models have been developed that can solve diverse types of location allocation problems. Recently, several methods have been developed to solve different location allocation problems within the unified vector assignment ordered median problem (VAOMP) model. These methods combine P-Median and Coverage models, based on the tabu search metaheuristic algorithm. The present study uses the unified VAOMP model, integrated GIS, and both tabu search (TS) and simulated annealing (SA) metaheuristic algorithms to solve location allocation problems. The study assesses its findings in two different scenarios for fire stations. The results of applying the two algorithms in terms of time, the number of covered demands, and the quality of the solutions were examined. Comparisons showed that the TS algorithm was faster in solving P-Median problems and generated more qualitative solutions than SA. However, the SA algorithm had less runtime in Coverage and P-Center problems. The results also showed that the VAOMP model is a qualified model in the field of location allocation, which can be used in various fields, in particular, to examine the status of urban facilities in achieving social justice and urban sustainable development.

Recommender systems analyze conditions and user behaviors to recommend proportional services to users. Since the aim of such systems is to provide the most appropriate services, it appears essential to use filtering techniques to limit recommender items. In this study, spatial criteria such as distance, movement direction, visibility, and topological relationships were employed as filtering tools to provide the right items. Our model creates appropriate items for better recommendation based on spatial relationships between users and the surrounding service sites. This method demonstrates that the number of recommended items can be limited by considering the shortest distance from the service centers intended by users and taking user direction into account. Moreover, appropriate service centers can be proposed with respect to user visibility. In this study, topological relationships between user location and near places were used as spatial filters, too. Further, if these filters can interact with the environment in the same way as humans, it can be expected the recommendation process to improve. Thus, our model uses the fuzzy approach to help the system to perceive the uncertainty of the spatial linguistic terms. To evaluate the performance and effectiveness of our proposed spatial filtering model, we conduct several experiments on real datasets that were obtained from tracking the users’ location through GPS. Considering the actual conditions, this system solved the cold start problem using spatial filtering model. Experimental results show that 68% of test users considered our recommendations as relevant in new item cold start problem. Moreover, results reveal that compared with an LA-LDA model, using spatial filtering in cold start item problem is more robust.

Task allocation in uncertainty conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent- based simulation to investigate tasks allocation through the consideration of appropriate spatial strategies to deal with uncertainty in urban search and rescue (USAR) operation. The proposed method is presented in five phases: ordering existing tasks, finding coordinating agent, holding an auction, applying allocation strategies, and implementation and observation of environmental uncertainties. The methodology was evaluated in Tehran's District 1 for 6.6, 6.9, and 7.2 magnitude earthquakes. The simulation started by calculating the number of injured individuals, which was 28856, 73195 and 111463 people for each earthquake, respectively. The Simulations were performed for each scenario for a variety of rescuers (1000, 1500, 2000 rescuer). In comparison with contract net protocol (CNP), the standard time of rescue operations in the proposed approach includes at least 13% of improvement and the best percentage of recovery was 21 %. Interval uncertainty analysis and the comparison of the proposed strategies showed that an increase in uncertainty leads to an increased rescue time for CNP of 67.7 hours, and for strategies one to four an increased rescue time of 63.4, 63.2, 63.7, and 56.5 hours, respectively. Considering strategies in the task allocation process, especially spatial strategies, resulted in the optimization and increased flexibility of the allocation as well as conditions for fault tolerance and agent-based cooperation stability in USAR simulation system.

Objective This study pursues three main objectives: 1) exploring the spatial distribution patterns of human brucellosis (HB); 2) identifying parameters affecting the disease spread; and 3) modeling and predicting the spatial distribution of HB cases in 2012–2016 and 2017–2018, respectively, in rural districts of Mazandaran province, Iran. Methods We collected data on the disease incidence, demography, ecology, climate, topography, and vegetation. Using the Global Moran’s I statistic, we measured spatial autocorrelation between log (number of HB cases). We applied the Getis-Ord G i * statistic to identify areas with high and low risk of the disease. To investigate the relationships between the factors affecting the incidence of HB as input variables together and the factors with the log (number of HB cases) as an output variable, we used the statistical linear regression model and the Pearson correlation coefficient. Then, we implemented a GIS-based adaptive neuro-fuzzy inference system (ANFIS) with two subtractive clustering and fuzzy c-means (FCM) clustering methods to model and predict the spatial distribution of HB. Results Global Moran's I spatial autocorrelation analysis indicated that the type of HB distribution is clustered in all years except 2014 and 2017, which are random. According to the Getis-Ord G i * analysis, the location of the hot spots varied during 2012–2018. In 2012 and 2013, most of the hot spots were seen in the west of the province. While in 2018, they were mostly concentrated in the eastern regions of the province. The linear regression model indicated that the parameters affecting the incidence of HB are independent of each other and can explain only 25.3% of the total changes in the log (number of HB cases). The results of the Pearson correlation coefficient showed that there were positive relationships between vegetation, log (population), and the number of sheep and cattle (p-value Conclusion The findings may have important implications for public health. The emergence of the hot spots in the east of the province can be a warning to the health system. Health authorities can use the findings of this study to predict the spread of HB and perform HB prevention programs. They can also investigate the factors affecting the prevalence of the disease, identify high-risk areas, and ultimately allocate resources to high-risk regions.

A new raster-based least-cost path analysis algorithm is proposed in this article that considers the critical length of grades as a parameter-governing factor that ascertains whether a path is traversable in hilly terrains. Our proposed algorithm uses a speed prediction model to predict the speed of trucks after each path segment based on an initial speed, gradient value, and length of the segment. We also consider earthwork operations, slope thresholds, and moving-window models. After applying the proposed algorithm to real-world data, a traversable path is obtained; previous studies cannot guarantee such a capability. By comparing this proposed algorithm with the latest least-cost path algorithm, we found that it offers a longer path in upward slopes to compensate for the speed of trucks. Speed profiles also reveal that trucks cannot traverse paths suggested by the existing algorithm in hilly terrains, and they stop in the middle of the road. However, in the proposed algorithm, vehicles traverse the path while compensating for speed on upward slopes. This algorithm can be used by road designers in GIS software.

Recommender systems (RS), as supportive tools, filter information from a massive amount of data based on the determined preferences. Most of the RS require information about the context of users su...

The use of web services for analysing and visualising maps has received great attention recently, because the complicated analysis of spatial data requires different processes to be run consecutively. Predicting the future location of a vehicle on a street network is one of the most challenging analyses used for improving context-aware location-based services, intelligent transportation systems and criminology. In this research, the authors present a new short-term prediction algorithm and explore the required analyses and web services. They present an appropriate method for chaining these web services to predict location(s). To assess their methodology, they developed a prototype system and tested for trajectories in Beijing. This system calculates the prediction time for a specified car to show the predicted future location in the street network. Their results showed that the average transferred data volume increases as the prediction period increases. The results also showed that the prediction algorithm has 75% accuracy at 1 min and 87.5% accuracy at 2 and 3 min. The implemented chaining method reduces the complexity of the location prediction algorithm for users because they do not need to know the processes. The outputs from this system can be used as input parameters for other web-based applications.

Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Electricity has a significant role in the sustainable development of societies. Traditional methods of generating electricity face several challenges. The ever-increasing demand for electricity generation on the one hand, and the lack of adequate resources for fossil fuels on the other, has led to the use of renewable energy. Biomass is a renewable energy supply that can be used in electricity generation for a sustainable environment. This study aims to introduce a multi-criteria decision-making framework which integrates a geographical information system with a fuzzy analytic network process together with weighted linear combination to optimize the location of a biomass power plant in Guilan Province, Iran. For this purpose, the environmental and socioeconomic factors were identified, and the main contributing criteria were selected. The results showed that about 27.73 and 41.06% of the region has high or moderate suitability, respectively, for constructing a biomass power plant. In addition, a sensitivity analysis was performed to investigate the robustness of the outcomes of decision making by changing the weighting of the criteria. Results indicated that the ranking of alternative locations is independent of the weights chosen.

This paper examines physical vulnerability of District No. 6 of Tehran to blast damages and blast loads. The main objectives are to enhance planners and decision makers' awareness of the extent of vulnerability of buildings in the study area (under different risk scenarios) and to provide guidance on how to enhance preparedness against potential risks. Physical vulnerability of District No. 6 of Tehran was evaluated under three different blast loading scenarios. An expert survey, involving specialists with expertise in urban development, passive defense, construction, and architecture was carried out to extract fourteen vulnerability criteria. Fuzzy criteria maps corresponding to each of these criteria were produced in Geospatial Information Systems (GIS) environment. Fuzzy analytic hierarchy processing was used to weigh the criteria. Criterion maps were combined using the fuzzy weighted linear combination operator. Finally, Ordered Weighted Averaging (OWA) operator was applied to generate final vulnerability maps. Results show that, for all scenarios, almost 70% of the buildings in the district are of high vulnerability. Sensitivity analysis confirms the reliability of the model.

Iran, as a seismic country, is situated over the Himalayan-Alpied seismic belt and has faced many destructive earthquakes throughout history. Therefore, it is very important to evaluate the possible damage to the existing infrastructure based on statistical and spatial analysis. In this study, a new model is developed to analyse seismic damages based on seismic hazard assessment and extraction of the vulnerability function for all features of fuel infrastructure. To consider uncertainty analysis in the model, Monte Carlo simulation is used based on 10,000 iterations. The results of hazard analysis indicated that peak ground acceleration is about 0.18 g and there is slight to moderate damages to the desired fuel infrastructure in the study area. Moreover, sensitivity analysis is also performed to determine how median, standard deviation (or beta), grid size, attenuation relationships, liquefaction and landslide susceptibility impact the seismic loss. Last but not least, the effect of input parameters of earthquake scenarios including magnitude, focal depth and focal distance are also analysed in conjunction with regression analysis. The results of the study show that magnitude and focal distance are the most sensitive parameters in which the expected damage to the fuel infrastructure is reduced by about 25% if the epicentre of the earthquake is moved from 10 to 25 km.

The uncertainty of task allocation in disaster environments is challenging both practically and theoretically. In real environments, agents encounter uncertain factors for the selection and execution of tasks. The present methods for task allocation seem inadequate in such environments. This paper aims to provide an efficient approach to improving the task allocation, despite the uncertainty in disaster environments. Therefore, after deduction of the major uncertainties in disaster environments, we propose a method for the agents’ decision-making about the task allocation. The allocation procedure includes four phases of ordering the tasks, choosing the coordinating agent, implementing the auction by considering the uncertainties, performing tasks and observing the real environment. The main innovation of this research is using the concepts of interval uncertainty in the task ordering as well as in the auction implementing. The results were obtained by comparing the proposed method with the contract net protocol (CNP) at three scales. In addition, the results were evaluated in the presence of uncertainties at different ranges. On average, the proposed method was better than the CNP in terms of search and rescue (SAR) operation time (124 min), the number of dead people (8) and the number of incorrect allocations (180 tasks). The uncertainties range in the tasks’ decision-making procedure affected SAR operation time by more than 26%. Therefore, considering that uncertainty in task allocation can be a great advantage in the disaster environment, and considering these factors in the decision-making procedure, we have improved confidence in allocating tasks with fewer errors.

Objective: To investigate the associations between the brucellosis and four climatic factors including temperature, precipitation, wind speed and greenness for better understanding the epidemiology of the disease in Iran during April 2009 to March 2012. Methods: A cross-sectional survey was performed on 39 359 recorded cases during the study period. Pearson’s correlation coefficient was used to investigate statistically meaningful temporal and spatial relevance between the disease and parameters. Besides, multiple linear regression was applied to estimate the best combination of the variables for predicting brucellosis incidence. Results: Pearson’s analysis revealed that there are positive temporal correlations between incidence and temperature, wind speed and greenness. Besides, a strong negative temporal association was observed with precipitation. Although a remarkable negative spatial association was observed between aggregated incidence rates and vegetation cover of corresponding counties in winter, this correlation was strongly positive for spring and summer. Conclusions: The prevalence of brucellosis is considerably affected by climatic conditions. Locations with higher greenness, temperature and wind speed are more susceptible to the disease. In contrast, areas with lower rainfall tend to face surpassing rates. Additionally, greater rates are expected for counties having green springs and summers, with dry winters.

Analyzing the spatial behaviors of moving-point objects (MPOs) and discovering their movement patterns have been of great interest to the geographic information science community recently. These interests can be explored through analyzing similarities in the MPO trajectories. Because movements of objects take place in various contexts, their trajectories are also highly influenced by such contexts. Therefore, it is essential to fully understand the contexts and to realize how they can be incorporated into movement analysis. This article first proposes a taxonomy for contexts. Then, a modified version of dynamic time warping called context-based dynamic time warping (CDTW) is introduced, to contextually assess the multidimensional weighted similarities of trajectories. Ultimately, the results of similarity searches are utilized in discovering the relative movement patterns of the MPOs. To evaluate the performance and effectiveness of our proposed CDTW method, we run several experiments on real datasets tha...

The first step in any seismic hazard study is the definition of seismogenic sources and the estimation of magnitude-frequency relationships for each source. There is as yet no standard methodology for source modeling and many researchers have worked on this topic. This study is an effort to define linear and area seismic sources for Northern Iran. The linear or fault sources are developed based on tectonic features and characteristic earthquakes while the area sources are developed based on spatial distribution of small to moderate earthquakes. Time-dependent recurrence relationships are developed for fault sources using renewal approach while time-independent frequency-magnitude relationships are proposed for area sources based on Poisson process. GIS functionalities are used in this study to introduce and incorporate spatial-temporal and geostatistical indices in delineating area seismic sources. The proposed methodology is used to model seismic sources for an area of about 500 by 400 square kilometers around Tehran. Previous researches and reports are studied to compile an earthquake/fault catalog that is as complete as possible. All events are transformed to uniform magnitude scale; duplicate events and dependent shocks are removed. Completeness and time distribution of the compiled catalog is taken into account. The proposed area and linear seismic sources in conjunction with defined recurrence relationships can be used to develop time-dependent probabilistic seismic hazard analysis of Northern Iran.

A mutual interaction between citizens and government has become an important requirement for every successful urban decision making process. As cities develop, such interactions play a greater role in citizen satisfaction. Through these interactions, governments can recognize the needs of citizens and try to address their challenges. In these interactions, the role of the people is central; in fact, it is the people who express their needs to the service-providing organizations through different communication channels. A common channel that has been established in Iran is the ‘137 center,’ a municipal, phone-based request/response system. In this article, information from Municipal District 1 in the city of Bojnourd was gathered, analyzed to prioritize urban needs and assessed for citizen satisfaction. Forty-three citizen needs were identified and categorized based on K-means clustering, the Fuzzy Clustering Method (FCM) and the Imperialist Competitive Algorithm (ICA). The three algorithms were also evaluated. RFM (recency, frequency, monetary) analysis was performed for classification. The clustering methods were then assessed and compared using three parameters: execution time, accuracy and simplicity. The results of the FCM and ICA clustering were similar, however, the execution time for FCM was less than for ICA. Considering the similarity of the results and the flexibility of FCM, it was concluded that, if the execution time was of primary importance, then the use of FCM was more appropriate. In contrast, if accuracy was a priority, ICA was preferred. Our results also showed that if simplicity and speed were required, the K-means algorithm was the best choice. Finally, subjects such as the quality of the asphalt, garbage collection and park development were of primary importance to Bojnourd citizens, therefore the municipality should pay special attention to these subjects.

Urban sprawl has become a remarkable feature in urban development, especially in developing countries, in the last decades. To face this phenomenon, it is required to first forecast auto-spreading ...

Recent advances in spatial data collection technologies and online services dramatically increase the contribution of ordinary people to produce, share, and use geographic information. Collecting spatial data as well as disseminating them on the internet by citizens has led to a huge source of spatial data termed as Volunteered Geographic Information (VGI) by Mike Goodchild. Although, VGI has produced previously unavailable data assets, and enriched existing ones. But its quality can be highly variable and challengeable. This presents several challenges to potential end users who are concerned about the validation and the quality assurance of the data which are collected. Almost, all the existing researches are based on how to find accurate VGI data from existing VGI data which consist of a) comparing the VGI data with the accurate official data, or b) in cases that there is no access to correct data; therefore, looking for an alternative way to determine the quality of VGI data is essential, and so forth. In this paper it has been attempt to develop a useful method to reach this goal. In this process, the positional accuracy of linear feature of Iran, Tehran OSM data have been analyzed.

Air pollutants and allergens are the main stimuli that have considerable effects on asthmatic patients' health. Seamless monitoring of patients' conditions and the surrounding environment, limiting their exposure to allergens and irritants, and reducing the exacerbation of symptoms can aid patients to deal with asthma better. In this context, ubiquitous healthcare monitoring systems can provide any service to any user everywhere and every time through any device and network. In this regard, this research established a GIS-based outdoor asthma monitoring framework in light of ubiquitous systems. The proposed multifaceted model was designed in three layers: (1) pre-processing, for cleaning and interpolating data, (2) reasoning, for deducing knowledge and extract contextual information from data, and (3) prediction, for estimating the asthmatic conditions of patients ubiquitously. The effectiveness of the proposed model is assessed by applying it on a real dataset that comprised of internal context information including patients' personal information (age, gender, height, medical history), patients' locations, and their peak expiratory flow (PEF) values, as well as external context information including air pollutant data (O3, SO2, NO2, CO, PM10), meteorological data (temperature, pressure, humidity), and geographic information related to the city of Tehran, Iran. With more than 92% and 93% accuracies in reasoning and estimation mechanism, respectively, the proposed method showed remarkably effective in asthma monitoring and management.