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Ali Asghar Alesheikh
Department of Geospatial Information System (GIS), K. N. Toosi University of Technology, Tehran, Iran

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Original paper
Published: 26 June 2021 in Applied Geomatics
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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.

ACS Style

Reza Shirzad; Ali Asghar Alesheikh; Mohsen Ahmadkhani; Saied Reza Naddaf. Aedes albopictus: a spatial risk mapping of the mosquito using geographic information system in Iran. Applied Geomatics 2021, 1 -10.

AMA Style

Reza Shirzad, Ali Asghar Alesheikh, Mohsen Ahmadkhani, Saied Reza Naddaf. Aedes albopictus: a spatial risk mapping of the mosquito using geographic information system in Iran. Applied Geomatics. 2021; ():1-10.

Chicago/Turabian Style

Reza Shirzad; Ali Asghar Alesheikh; Mohsen Ahmadkhani; Saied Reza Naddaf. 2021. "Aedes albopictus: a spatial risk mapping of the mosquito using geographic information system in Iran." Applied Geomatics , no. : 1-10.

Journal article
Published: 09 May 2021 in Acta Tropica
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: 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. : 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 Gi* 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. : 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 Gi* 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 < 0.05). The above-mentioned factors had the strongest positive correlation with the log (number of HB cases) (P-value < 0.01). These results may be due to the fact that vegetation regions are suitable for livestock grazing, attracting large crowds of people. Therefore, this will increase HB cases. We compared the results of subtractive clustering and FCM clustering methods by evaluation criteria (e.g., linear correlation coefficient (LCC) and mean absolute error (MAE)) in two phases of development and assessment of the ANFIS model. In the evaluation phase, we predicted the spatial distribution of log (number of HB cases) in 2017 and 2018 for subtractive clustering (R2 = 0.699, LCC or R = 0.692, MAE = 0.509, MSE = 0.455) and for FCM clustering (R2 = 0.704, LCC or R = 0.697, MAE = 0.512, MSE = 0.448) that showed FCM clustering outperformed the subtractive clustering. : 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.

ACS Style

Elnaz Babaie; Ali Asghar Alesheikh; Mohammad Tabasi. Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS). Acta Tropica 2021, 220, 105951 .

AMA Style

Elnaz Babaie, Ali Asghar Alesheikh, Mohammad Tabasi. Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS). Acta Tropica. 2021; 220 ():105951.

Chicago/Turabian Style

Elnaz Babaie; Ali Asghar Alesheikh; Mohammad Tabasi. 2021. "Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS)." Acta Tropica 220, no. : 105951.

Journal article
Published: 17 March 2021 in IEEE Access
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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.

ACS Style

Saeed Mehri; Ali Asghar Alesheikh; Anahid Basiri. A Contextual Hybrid Model for Vessel Movement Prediction. IEEE Access 2021, 9, 45600 -45613.

AMA Style

Saeed Mehri, Ali Asghar Alesheikh, Anahid Basiri. A Contextual Hybrid Model for Vessel Movement Prediction. IEEE Access. 2021; 9 (99):45600-45613.

Chicago/Turabian Style

Saeed Mehri; Ali Asghar Alesheikh; Anahid Basiri. 2021. "A Contextual Hybrid Model for Vessel Movement Prediction." IEEE Access 9, no. 99: 45600-45613.

Journal article
Published: 28 November 2020 in Applied Sciences
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The Vector Assignment Ordered Median Problem (VAOMP) is a new unified approach for location-allocation problems, which are one of the most important forms of applied analysis in GIS (Geospatial Information System). Solving location-allocation problems with exact methods is difficult and time-consuming, especially when the number of objectives and criteria increases. One of the most important criteria in location-allocation problems is the capacity of facilities. Firstly, this study develops a new VAOMP approach by including capacity as a criterion, resulting in a new model known as VAOCMP (Vector Assignment Ordered Capacitated Median Problem). Then secondly, the results of applying VAOMP, in scenario 1, and VAOCMP, in scenario 2, for the location-allocation of fire stations in Tehran, with the objective of minimizing the arrival time of fire engines to an incident site to no more than 5 min, are examined using both the Tabu Search and Simulated Annealing algorithms in GIS. The results of scenario 1 show that 52,840 demands were unable to be served with 10 existing stations. In scenario 2, given that each facility could not accept demand above its capacity, the number of demands without service increased to 59,080, revealing that the number of stations in the study area is insufficient. Adding 35 candidate stations and performing relocation-reallocation revealed that at least three other stations are needed for optimal service. Thirdly, and finally, the VAOMP and VAOCMP were implemented in a modest size problem. The implementation results for both algorithms showed that the Tabu Search algorithm performed more effectively.

ACS Style

Alireza Vafaeinejad; Samira Bolouri; Ali Asghar Alesheikh; Mahdi Panahi; Chang-Wook Lee. The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam). Applied Sciences 2020, 10, 8505 .

AMA Style

Alireza Vafaeinejad, Samira Bolouri, Ali Asghar Alesheikh, Mahdi Panahi, Chang-Wook Lee. The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam). Applied Sciences. 2020; 10 (23):8505.

Chicago/Turabian Style

Alireza Vafaeinejad; Samira Bolouri; Ali Asghar Alesheikh; Mahdi Panahi; Chang-Wook Lee. 2020. "The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam)." Applied Sciences 10, no. 23: 8505.

Journal article
Published: 11 November 2020 in Parasites & Vectors
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Background Zoonotic cutaneous leishmaniasis (ZCL) is a neglected tropical disease worldwide, especially the Middle East. Although previous works attempt to model the ZCL spread using various environmental factors, the interactions between vectors (Phlebotomus papatasi), reservoir hosts, humans, and the environment can affect its spread. Considering all of these aspects is not a trivial task. Methods An agent-based model (ABM) is a relatively new approach that provides a framework for analyzing the heterogeneity of the interactions, along with biological and environmental factors in such complex systems. The objective of this research is to design and develop an ABM that uses Geospatial Information System (GIS) capabilities, biological behaviors of vectors and reservoir hosts, and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model to explore the spread of ZCL. Various scenarios were implemented to analyze the future ZCL spreads in different parts of Maraveh Tappeh County, in the northeast region of Golestan Province in northeastern Iran, with alternative socio-ecological conditions. Results The results confirmed that the spread of the disease arises principally in the desert, low altitude areas, and riverside population centers. The outcomes also showed that the restricting movement of humans reduces the severity of the transmission. Moreover, the spread of ZCL has a particular temporal pattern, since the most prevalent cases occurred in the fall. The evaluation test also showed the similarity between the results and the reported spatiotemporal trends. Conclusions This study demonstrates the capability and efficiency of ABM to model and predict the spread of ZCL. The results of the presented approach can be considered as a guide for public health management and controlling the vector population.

ACS Style

Mohammad Tabasi; Ali Asghar Alesheikh; Aioub Sofizadeh; Bahram Saeidian; Biswajeet Pradhan; Abdullah AlAmri. A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran. Parasites & Vectors 2020, 13, 1 -17.

AMA Style

Mohammad Tabasi, Ali Asghar Alesheikh, Aioub Sofizadeh, Bahram Saeidian, Biswajeet Pradhan, Abdullah AlAmri. A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran. Parasites & Vectors. 2020; 13 (1):1-17.

Chicago/Turabian Style

Mohammad Tabasi; Ali Asghar Alesheikh; Aioub Sofizadeh; Bahram Saeidian; Biswajeet Pradhan; Abdullah AlAmri. 2020. "A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran." Parasites & Vectors 13, no. 1: 1-17.

Journal article
Published: 04 November 2020 in Annals of GIS
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ACS Style

Danial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. Prediction of vessels locations and maritime traffic using similarity measurement of trajectory. Annals of GIS 2020, 1 -12.

AMA Style

Danial Alizadeh, Ali Asghar Alesheikh, Mohammad Sharif. Prediction of vessels locations and maritime traffic using similarity measurement of trajectory. Annals of GIS. 2020; ():1-12.

Chicago/Turabian Style

Danial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. 2020. "Prediction of vessels locations and maritime traffic using similarity measurement of trajectory." Annals of GIS , no. : 1-12.

Regular paper
Published: 12 September 2020 in Knowledge and Information Systems
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In recent years, point of interest (POI) recommendation has gained increasing attention all over the world. POI recommendation plays an indispensable role in assisting people to find places they are likely to enjoy. The exploitation of POIs recommendation by existing models is inadequate due to implicit correlations among users and POIs and cold start problem. To overcome these problems, this work proposed a social spatio-temporal probabilistic matrix factorization (SSTPMF) model that exploits POI similarity and user similarity, which integrates different spaces including the social space, geographical space and POI category space in similarity modelling. In other words, this model proposes a multivariable inference approach for POI recommendation using latent similarity factors. The results obtained from two real data sets, Foursquare and Gowalla, show that taking POI correlation and user similarity into account can further improve recommendation performance. In addition, the experimental results show that the SSTPMF model performs better in alleviating the cold start problem than state-of-the-art methods in terms of normalized discount cumulative gain on both data sets.

ACS Style

Mehri Davtalab; Ali Asghar Alesheikh. A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization. Knowledge and Information Systems 2020, 63, 65 -85.

AMA Style

Mehri Davtalab, Ali Asghar Alesheikh. A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization. Knowledge and Information Systems. 2020; 63 (1):65-85.

Chicago/Turabian Style

Mehri Davtalab; Ali Asghar Alesheikh. 2020. "A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization." Knowledge and Information Systems 63, no. 1: 65-85.

Originalpaper
Published: 01 September 2020 in Doklady Earth Sciences
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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.

ACS Style

Zahra Chatrsimab; Ali Asghar Alesheikh; Behzad Voosoghi; Saeed Behzadi; Mehdi Modiri. Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer, Iran). Doklady Earth Sciences 2020, 494, 718 -725.

AMA Style

Zahra Chatrsimab, Ali Asghar Alesheikh, Behzad Voosoghi, Saeed Behzadi, Mehdi Modiri. Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer, Iran). Doklady Earth Sciences. 2020; 494 (1):718-725.

Chicago/Turabian Style

Zahra Chatrsimab; Ali Asghar Alesheikh; Behzad Voosoghi; Saeed Behzadi; Mehdi Modiri. 2020. "Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer, Iran)." Doklady Earth Sciences 494, no. 1: 718-725.

Journal article
Published: 26 August 2020 in Journal of Navigation
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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.

ACS Style

Danial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. Vessel Trajectory Prediction Using Historical Automatic Identification System Data. Journal of Navigation 2020, 74, 156 -174.

AMA Style

Danial Alizadeh, Ali Asghar Alesheikh, Mohammad Sharif. Vessel Trajectory Prediction Using Historical Automatic Identification System Data. Journal of Navigation. 2020; 74 (1):156-174.

Chicago/Turabian Style

Danial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. 2020. "Vessel Trajectory Prediction Using Historical Automatic Identification System Data." Journal of Navigation 74, no. 1: 156-174.

Original paper
Published: 05 August 2020 in Arabian Journal of Geosciences
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In crowded cities, like Tehran, when a major accident occurs, such as a fire, the response from more than one fire station is usually needed at the scene. The present study focuses on demand allocation to fire stations at two ranked levels to determine the priorities of fire stations to service relevant demands. To solve this problem, this paper uses the Vector Assignment Ordered Median Problem (VAOMP), a new location–allocation model that can allocate demands to facilities at several ranked levels, based on the particular objective function. Thus, this paper uses the meta-heuristic methods of Tabu and genetic algorithms to minimize the arrival time from fire stations to demands, at two levels, at up to 5 min in the GIS environment of the 21st and 22nd districts of Tehran. The optimum parameters for each algorithm were obtained through sensitivity analysis. The results of applying the model with two algorithms in these districts with 10 existing fire stations and 336,600 inhabitants showed that the current stations are insufficient for two levels of service and that 52,840 people at level 1 and 81,320 people at level 2 have no access to services. As such, the results of two algorithms for relocation–reallocation analysis at two levels with different weightings for 13 potential and existing fire stations showed that at least 3 new stations need to be created. Furthermore, the genetic algorithm produced qualitatively superior results, in optimal values, the accuracy of allocation and timeframe, compared with the Tabu algorithm.

ACS Style

Samira Bolouri; Alireza Vafaeinejad; Aliasghar Alesheikh; Hossein Aghamohammadi. Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations. Arabian Journal of Geosciences 2020, 13, 1 -13.

AMA Style

Samira Bolouri, Alireza Vafaeinejad, Aliasghar Alesheikh, Hossein Aghamohammadi. Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations. Arabian Journal of Geosciences. 2020; 13 (16):1-13.

Chicago/Turabian Style

Samira Bolouri; Alireza Vafaeinejad; Aliasghar Alesheikh; Hossein Aghamohammadi. 2020. "Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations." Arabian Journal of Geosciences 13, no. 16: 1-13.

Journal article
Published: 01 December 2019 in Journal of Geospatial Information Technology
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ACS Style

Kamal Mohammadi; Ali Asghar Alesheikh; Mohammad Taleai; K.N. Toosi University of Technology. Locating Hospital Centers By an Integration of BWM، DANP، VIKOR and COPRAS Methods (Case Study: Region 1, City of Tehran). Journal of Geospatial Information Technology 2019, 7, 17 -42.

AMA Style

Kamal Mohammadi, Ali Asghar Alesheikh, Mohammad Taleai, K.N. Toosi University of Technology. Locating Hospital Centers By an Integration of BWM، DANP، VIKOR and COPRAS Methods (Case Study: Region 1, City of Tehran). Journal of Geospatial Information Technology. 2019; 7 (3):17-42.

Chicago/Turabian Style

Kamal Mohammadi; Ali Asghar Alesheikh; Mohammad Taleai; K.N. Toosi University of Technology. 2019. "Locating Hospital Centers By an Integration of BWM، DANP، VIKOR and COPRAS Methods (Case Study: Region 1, City of Tehran)." Journal of Geospatial Information Technology 7, no. 3: 17-42.

Original paper
Published: 28 October 2019 in International Journal of Environmental Science and Technology
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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.

ACS Style

S. Bolouri; A. Vafeainejad; A. Alesheikh; H. Aghamohammadi. Environmental sustainable development optimizing the location of urban facilities using vector assignment ordered median problem-integrated GIS. International Journal of Environmental Science and Technology 2019, 17, 3033 -3054.

AMA Style

S. Bolouri, A. Vafeainejad, A. Alesheikh, H. Aghamohammadi. Environmental sustainable development optimizing the location of urban facilities using vector assignment ordered median problem-integrated GIS. International Journal of Environmental Science and Technology. 2019; 17 (5):3033-3054.

Chicago/Turabian Style

S. Bolouri; A. Vafeainejad; A. Alesheikh; H. Aghamohammadi. 2019. "Environmental sustainable development optimizing the location of urban facilities using vector assignment ordered median problem-integrated GIS." International Journal of Environmental Science and Technology 17, no. 5: 3033-3054.

Journal article
Published: 19 October 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Brucellosis is one of the most important zoonotic diseases which is endemic in Iran. This disease is considered a significant hazard to citizens’ health and imposes heavy economic burdens, hence, requires a thorough control and management plan. The aims of this study are identifying the areas having the highest risk of brucellosis, as well as discovering the contributing environmental factors. The maximum entropy (MaxEnt) method was used to model the probability of brucellosis in Golestan, Mazandaran, and Guilan provinces. The possible contribution of 12 environmental parameters in this disease was also measured using the Jackknife method. The results showed that the highest risk of brucellosis is located in southern Golestan, East, and West of Mazandaran, and south of Guilan province, and moisture, slope, vegetation and elevation are the most effective environmental factors on the spatial distribution of the disease. In addition, the probability of the disease in northern Iran increases from west to east. These findings could assist the public health managers and decision-makers in organizing a more efficient public health system.

ACS Style

N. Seyedalizadeh; A. A. Alesheikh; M. Ahmadkhani. SPATIO-STATISTICAL MODELING OF HUMAN BRUCELLOSIS USING ENVIRONMENTAL PARAMETERS: A CASE STUDY OF NORTHERN IRAN. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W18, 969 -973.

AMA Style

N. Seyedalizadeh, A. A. Alesheikh, M. Ahmadkhani. SPATIO-STATISTICAL MODELING OF HUMAN BRUCELLOSIS USING ENVIRONMENTAL PARAMETERS: A CASE STUDY OF NORTHERN IRAN. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W18 ():969-973.

Chicago/Turabian Style

N. Seyedalizadeh; A. A. Alesheikh; M. Ahmadkhani. 2019. "SPATIO-STATISTICAL MODELING OF HUMAN BRUCELLOSIS USING ENVIRONMENTAL PARAMETERS: A CASE STUDY OF NORTHERN IRAN." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18, no. : 969-973.

Journal article
Published: 18 October 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The participation of citizens in decision-making processes is one of the main concerns in urban planning. People’s contributions increase the usability and efficiency of urban facilities. Hospitals and healthcare services are among the most important public facilities that citizens require. This paper aims to improve an approach that could locate the hospitals according to the citizens’ preferences. Decision-making process in this situation should consider the uncertainties exist in any steps of decisions-making. In this regard, this paper applied Fuzzy-VIKOR method that is appropriate to model such kind of uncertainty. The proposed method was accomplished in Districts 6 of Tehran province. The achieved results were compared with each other in two different scenario (using expert knowledge and citizens’ satisfaction). The comparison of the results showed that the more suitable distribution and density of proposed sites for hospitals must be observed if the citizens’ perspectives were considered. Also, the proposed sites with experts follow urban planning principals rather than the second case.

ACS Style

Z. Neisani Samani; A. A. Alesheikh. UNCERTAINTY MODELLING OF CITIZEN-CENTERED GROUP DECISION MAKING USING FUZZY-VIKOR CASE STUDY: SITE SELECTION OF HEALTHCARE SERVICES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W18, 809 -814.

AMA Style

Z. Neisani Samani, A. A. Alesheikh. UNCERTAINTY MODELLING OF CITIZEN-CENTERED GROUP DECISION MAKING USING FUZZY-VIKOR CASE STUDY: SITE SELECTION OF HEALTHCARE SERVICES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W18 ():809-814.

Chicago/Turabian Style

Z. Neisani Samani; A. A. Alesheikh. 2019. "UNCERTAINTY MODELLING OF CITIZEN-CENTERED GROUP DECISION MAKING USING FUZZY-VIKOR CASE STUDY: SITE SELECTION OF HEALTHCARE SERVICES." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18, no. : 809-814.

Journal article
Published: 18 October 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Location-allocation analysis is one of the most GIS useful analysis, especially in allocating demands to facilities. One of these facilities is the fire stations, which the correct locations and optimal demand allocations to those have most importance. Each facility has a specific capacity that should be considered in locating the facilities and allocating the demand to those. In recent years, the use of unified models in solving allocation problems is too common because these models can solve a variety of problems, but in most of these models, the capacity criterion for facilities has been ignored. The present study tries to investigate the location-allocation problem of the fire stations with the aid of two Tabu and Genetic algorithms with the goal of maximizing the coverage using the (Vector Assignment Ordered Median Problem) VAOMP model, taking into account the capacity criterion and regardless of it. The results of using two algorithms in problem-solving show that the Genetic algorithm produces better quality solutions over a shorter time. Also, considering the capacity criterion that brings the problem closer to real-world space, in the study area, 59,640 demands will not be covered by any station within a 5-minute radius and will be highly vulnerable to potential hazards. Also, by adding 3 stations to the existing stations and re-allocating, the average of allocated demands with the help of Genetic was 93.39% and 92.74% for the Tabu algorithm. So it is necessary to consider the capacity of the facilities for optimal services.

ACS Style

S. Bolouri; A. Vafaeinejad; A. Alesheikh; H. Aghamohammadi. INVESTIGATING THE EFFECT OF CAPACITY CRITERION ON THE OPTIMAL ALLOCATION OF EMERGENCY FACILITIES IN GIS ENVIRONMENT. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W18, 211 -217.

AMA Style

S. Bolouri, A. Vafaeinejad, A. Alesheikh, H. Aghamohammadi. INVESTIGATING THE EFFECT OF CAPACITY CRITERION ON THE OPTIMAL ALLOCATION OF EMERGENCY FACILITIES IN GIS ENVIRONMENT. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W18 ():211-217.

Chicago/Turabian Style

S. Bolouri; A. Vafaeinejad; A. Alesheikh; H. Aghamohammadi. 2019. "INVESTIGATING THE EFFECT OF CAPACITY CRITERION ON THE OPTIMAL ALLOCATION OF EMERGENCY FACILITIES IN GIS ENVIRONMENT." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18, no. : 211-217.

Journal article
Published: 18 October 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Drought has always been a heavy financial burden on a country's economy. To mitigate the impacts of an ongoing drought, spatial information on high risk areas are required. Therefore, the aim of this study is to identify the areas prone to drought in order to minimize the damages through a proper management policy. To achieve this goal, the actual evapotranspiration is considered. The rate of actual evapotranspiration was measured in Najaf Abad using the Surface Energy Balance Algorithm for Land (SEBAL) on Landsat ATM+ images. To present and test the changes in drought, the Change Vector Analysis (CVA) technique was applied to multi-temporal data to compare the differences in the time-trajectory of the tasselled cap intestines and brightness for two time periods 1995 to 2008 – 2008 to 2015. The images were processed using ArcGIS 10.3 and ERDAS Imagine 8.6™ software. The results indicated that the changed area is 9691.41 hectare and the unchanged is 49335.2 hectare between 1995 to 2008. The area of the changed pixels between 2008 and 2015, 3% is higher than the area of the change pixels in 1995 until 2008. In more than 3-quarter of the study area, the value of evapotranspiration has not changed. The proposed method demonstrated immense potentials in monitoring drought change dynamics especially when complemented with field studies.

ACS Style

R. Ebrahimian; A. Alesheikh. A CHANGE VECTOR ANALYSIS METHOD TO MONITOR DROUGHT USING LANDSAT DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W18, 321 -325.

AMA Style

R. Ebrahimian, A. Alesheikh. A CHANGE VECTOR ANALYSIS METHOD TO MONITOR DROUGHT USING LANDSAT DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W18 ():321-325.

Chicago/Turabian Style

R. Ebrahimian; A. Alesheikh. 2019. "A CHANGE VECTOR ANALYSIS METHOD TO MONITOR DROUGHT USING LANDSAT DATA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18, no. : 321-325.

Journal article
Published: 08 September 2019 in Applied Sciences
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The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

ACS Style

Wei Chen; Haoyuan Hong; Mahdi Panahi; Himan Shahabi; Yi Wang; Ataollah Shirzadi; Saied Pirasteh; Ali Asghar Alesheikh; Khabat Khosravi; Somayeh Panahi; Fatemeh Rezaie; Shaojun Li; Abolfazl Jaafari; Dieu Tien Bui; Baharin Bin Ahmad. Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Applied Sciences 2019, 9, 3755 .

AMA Style

Wei Chen, Haoyuan Hong, Mahdi Panahi, Himan Shahabi, Yi Wang, Ataollah Shirzadi, Saied Pirasteh, Ali Asghar Alesheikh, Khabat Khosravi, Somayeh Panahi, Fatemeh Rezaie, Shaojun Li, Abolfazl Jaafari, Dieu Tien Bui, Baharin Bin Ahmad. Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Applied Sciences. 2019; 9 (18):3755.

Chicago/Turabian Style

Wei Chen; Haoyuan Hong; Mahdi Panahi; Himan Shahabi; Yi Wang; Ataollah Shirzadi; Saied Pirasteh; Ali Asghar Alesheikh; Khabat Khosravi; Somayeh Panahi; Fatemeh Rezaie; Shaojun Li; Abolfazl Jaafari; Dieu Tien Bui; Baharin Bin Ahmad. 2019. "Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)." Applied Sciences 9, no. 18: 3755.

Journal article
Published: 23 August 2019 in Sustainability
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Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.

ACS Style

Mostafa Ghodousi; Ali Asghar Alesheikh; Bahram Saeidian; Biswajeet Pradhan; Chang-Wook Lee. Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data. Sustainability 2019, 11, 4595 .

AMA Style

Mostafa Ghodousi, Ali Asghar Alesheikh, Bahram Saeidian, Biswajeet Pradhan, Chang-Wook Lee. Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data. Sustainability. 2019; 11 (17):4595.

Chicago/Turabian Style

Mostafa Ghodousi; Ali Asghar Alesheikh; Bahram Saeidian; Biswajeet Pradhan; Chang-Wook Lee. 2019. "Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data." Sustainability 11, no. 17: 4595.

Journal article
Published: 06 June 2019 in Computers, Environment and Urban Systems
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Reverse geocoding is a process that maps coordinates to a set of location identifiers such as addresses or toponyms. What makes the reverse geocoding process challenging is the uncertainty of the position being asked and the point features used to represent places. In recent years, due to advances in locating technologies, large amounts of location-based data have been produced in location-based social networks such as the Yelp, Foursquare, and Swarm. These data are a rich source of information about the patterns of people's behaviors in different places. In this paper, with the help of these data, the enhancement of spatial distance-only reverse geocoding has been attempted. The main purpose of this paper is to develop and validate an algorithm for matching categories in the Yelp and Swarm services. In this way, the data from the Yelp were used for generating temporal behavior data and the data from Swarm were used for collecting check-in data. Since the data from Yelp and Swarm services have different categorization structures, integrating these two structures was one of the main challenges of our study. After matching the categories of Yelp and Swarm services, the obtained temporal behavior data for all data sets of Yelp were used in the process of reverse geocoding for Swarm check-in data. In our study, linear, rational and sinusoidal functions were used for distorting the spatial distance with temporal check-in probability in the process of reverse geocoding. In addition, two sets of data include training and test data were used for determining the parameters of the model and validating the results. In this way, it was found that by combining a linear model with temporal behavior data, the results of spatial distance-only reverse geocoding can be improved by 29.96% for the Mean Reciprocal Rank index (a statistical measure for evaluating any process that produces a list of responses, ordered by probability of correctness) and 105.73% for the First Position index (which counts the number of correctly identified POIs). The findings of our study confirmed that the extended set of temporal probabilities of POI categories obtained from Yelp and Swarm gives better results than previous studies. The strengths of our method was demonstrated by validating it against a spatial distance only baseline by the Mean Reciprocal Rank and the First Position indices.

ACS Style

Ali Sabzali Yameqani; Ali Asghar Alesheikh. Evaluating a location distortion model to improve reverse geocoding through temporal semantic signatures. Computers, Environment and Urban Systems 2019, 77, 101349 .

AMA Style

Ali Sabzali Yameqani, Ali Asghar Alesheikh. Evaluating a location distortion model to improve reverse geocoding through temporal semantic signatures. Computers, Environment and Urban Systems. 2019; 77 ():101349.

Chicago/Turabian Style

Ali Sabzali Yameqani; Ali Asghar Alesheikh. 2019. "Evaluating a location distortion model to improve reverse geocoding through temporal semantic signatures." Computers, Environment and Urban Systems 77, no. : 101349.

Journal article
Published: 22 April 2019 in Sustainable Cities and Society
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Increasing urbanization has been one of the most significant concerns of urban managers. The role of non-motorized transportation in sustainable urban development is vitally important for reducing overweight and obesity among citizens. These issues have led to numerous studies of the association between environmental characteristics, walkability index and levels of human health. The encouragement of public walking and cycling requires measures of walkability indices. One of the common challenges in measuring a walkability index is the complexity of the connection between the subjective indices resulting from public opinion and objective measures of geographic data. The scientific novelty of this paper lies in two aspects: First we developed and evaluated several artificial neural network (ANN) configurations for predicting subjective measures of walkability index from objective measures. Second, we introduced an index for two distinctive modes of walkability: daily shopping and recreation purposes that ranges from 1 to 10. The parameters of land-use diversity, population density, intersection density, network density, access to public transportation, green spaces and commercial places were utilized to calculate the objective value of the walkability index. The determination of subjective value of the walkability index was achieved using fieldwork reports. The resulting index was tested in districts 1 and 3 of Region 18 in the city of Tehran. The quantities used to evaluate the results included RMSE, MAE, MBE, and R. Network training was performed using the Levenberg-Marquardt algorithm. A 10-fold cross validation was used to evaluate and compare the performance of different network configurations. Our findings indicated that the best walkability index for purchasing can be estimated using Levenberg-Marquardt algorithm with one hidden layer and seven neurons. This configuration resulted in a correlation coefficient and an RMSE of 93.79% and 0.1368 respectively. To predict the walkability index for recreational purpose, the best result was obtained using Levenberg-Marquardt algorithm, representing a combination of one layer with four neurons for which the correlation coefficient and RMSE are 90.71% and 0.1602 respectively.

ACS Style

Ali Sabzali Yameqani; Ali Asghar Alesheikh. Predicting subjective measures of walkability index from objective measures using artificial neural networks. Sustainable Cities and Society 2019, 48, 101560 .

AMA Style

Ali Sabzali Yameqani, Ali Asghar Alesheikh. Predicting subjective measures of walkability index from objective measures using artificial neural networks. Sustainable Cities and Society. 2019; 48 ():101560.

Chicago/Turabian Style

Ali Sabzali Yameqani; Ali Asghar Alesheikh. 2019. "Predicting subjective measures of walkability index from objective measures using artificial neural networks." Sustainable Cities and Society 48, no. : 101560.