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Dr. Mahmoud Reza Delavar
Center of Excellence in Geomatic Eng. in Disaster Management, School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Iran.

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Research article
Published: 04 February 2021 in Journal of Location Based Services
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Nowadays, gathering information about commercial products can be performed either online or offline. While online searches can be virtually undertaken through online shopping websites, offline searches should be done physically at stores. However, there is a specific emerging trend where users can check some product opportunities online before getting to the stores and then possibly buying some items whose properties have already been evaluated over the Web. Product properties can be studied online while on site evaluation provides a direct contact with these goods at the stores. The objective of the approach developed in this paper is to discover user preferences when searching and exploring online shopping websites and then recommend the stores that better match their interests. First, users’ internet behaviours are extracted from an online shopping website. Secondly, a Voronoi high-dimensional structure supports the derivation of similarities between the users and stores. Third, a distance matrix between the user and the selected stores is generated. Finally, a ranked list of the most appropriate stores is provided to the users based on their product interest and their locations. The whole approach has been successfully tested by a panel of 30 volunteers in the 6th District of the city of Tehran.

ACS Style

Goshtasb Shahriari-Mehr; Mahmoud Reza Delavar; Christophe Claramunt; Babak Nadjar Araabi; Mohammad-Reza A. Dehaqani. A store location-based recommender system using user’s position and web searches. Journal of Location Based Services 2021, 15, 118 -141.

AMA Style

Goshtasb Shahriari-Mehr, Mahmoud Reza Delavar, Christophe Claramunt, Babak Nadjar Araabi, Mohammad-Reza A. Dehaqani. A store location-based recommender system using user’s position and web searches. Journal of Location Based Services. 2021; 15 (2):118-141.

Chicago/Turabian Style

Goshtasb Shahriari-Mehr; Mahmoud Reza Delavar; Christophe Claramunt; Babak Nadjar Araabi; Mohammad-Reza A. Dehaqani. 2021. "A store location-based recommender system using user’s position and web searches." Journal of Location Based Services 15, no. 2: 118-141.

Research article
Published: 26 October 2020 in Earth Science Informatics
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Land change models are amongst the most widely developed tools for spatial decision support. Despite this progress, only a few models have been created thus far that simulate urban growth that incorporate two important aspects of uncertainty inherent to land use dynamics: fuzziness and roughness. Combining fuzziness and roughness into models will enhance the use of these tools for decision support. This study applied and evaluated a fuzzy-based approach to the feature selection effects on the accuracy of a land change model. Fuzzy rough set theory (FRST) was employed here as feature selection method and was integrated with a support vector regression (SVR) algorithm to simulate urban growth of Tabriz mega city in northwest Iran. In order to apply feature selection to a FRST algorithm, incoming data has been first fuzzified by an adaptive neural fuzzy inference system (ANFIS). To evaluate the application of FRST, SVR was used with and without FRST (SVR and SVR-FRST), while for performance evaluation logistic regression (LR) and kernelled LR (KLR) models were integrated with and without FRST (LR, LR-FRST, KLR, and KLR-FRST). The accuracy of the simulated maps of all models were evaluated by calculating the overall accuracy (OA), true positive rate (TPR), true negative rate (TNR), total operating characteristic (TOC) and their area under curve (AUC). The results showed that integrating FRST with the above-mentioned models enhanced the overall performances based on the above criteria. Among the above mentioned models, SVM-FRST and KLR-FRST yielded the best goodness of fit measures. Moreover, SVM-FRST with 83.6% OA, 41.6% TPR, and 90.4% TNR performs better than KLR-FRST with 82.4% OA, 37.4% TPR, and 89.8% TNR. However, KLR-FRST has more AUC, less green area destruction, more barren to urban areas conversion, and fast tuning process related to SVR-FRST. Finally, we suggest that KLR-FRST and SVR-FRST are, among those evaluated, the most appropriate models for urban growth modelling of the Tabriz mega city of Iran when considering uncertainty.

ACS Style

D. Parvinnezhad; M. R. Delavar; B. C. Pijanowski; C. Claramunt. Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling. Earth Science Informatics 2020, 14, 17 -36.

AMA Style

D. Parvinnezhad, M. R. Delavar, B. C. Pijanowski, C. Claramunt. Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling. Earth Science Informatics. 2020; 14 (1):17-36.

Chicago/Turabian Style

D. Parvinnezhad; M. R. Delavar; B. C. Pijanowski; C. Claramunt. 2020. "Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling." Earth Science Informatics 14, no. 1: 17-36.

Articles
Published: 30 April 2020 in Journal of Urbanism: International Research on Placemaking and Urban Sustainability
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Carrying capacity can significantly affect both the density and heights of buildings in a particular area. While every country has its own approach to achieving equilibrium between building density and height, poor planning and violation of building height regulations can have a negative impact on urban structure and form. This paper presents a model that predicts the degree to which a new building construction affects urban landscape and density. The required parameters for the model were determined using the Analytic Hierarchy Process (AHP) and Delphi methods. The model produced rapid and accurate results with sample data for a medium-size city in Isfahan province, Iran which were then visually validated using three-dimensional visualisation in GIS environment. The model has the potential to facilitate the maintenance of equilibrium between building height and density. It can also assist to identify and prevent some violations of building height regulations in rapidly growing cities.

ACS Style

Asadallah Karimi; Mahmoud Reza Delavar; Mahmood Mohammadi; Payam Ghadirian. Spatial urban density modelling using the concept of carrying capacity: a case study of Isfahan, Iran. Journal of Urbanism: International Research on Placemaking and Urban Sustainability 2020, 13, 489 -512.

AMA Style

Asadallah Karimi, Mahmoud Reza Delavar, Mahmood Mohammadi, Payam Ghadirian. Spatial urban density modelling using the concept of carrying capacity: a case study of Isfahan, Iran. Journal of Urbanism: International Research on Placemaking and Urban Sustainability. 2020; 13 (4):489-512.

Chicago/Turabian Style

Asadallah Karimi; Mahmoud Reza Delavar; Mahmood Mohammadi; Payam Ghadirian. 2020. "Spatial urban density modelling using the concept of carrying capacity: a case study of Isfahan, Iran." Journal of Urbanism: International Research on Placemaking and Urban Sustainability 13, no. 4: 489-512.

Journal article
Published: 18 October 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Intelligent Transportation Systems (ITS) is one of the main components of a smart city. ITS have several purposes including the increase of the safety and comfort of the passengers and the reduction of the road accidents. ITS can enhance safety in three modes before, within and after the collision by preventing accident via assistive system, sensing the collision situation and calculating the time of the collision and providing the emergency response in a timely manner. The main objective of this paper is related to the smart transportation services which can be provided at the time of the collision and after the accident. After the accident, it takes several minutes to hours for the person to contact the emergency department. If an accident takes place for a vehicle in a remote area, this time increases and that may cause the loss of life. In addition, determination of the exact location of the accident is difficult by the emergency centres. That leads to the possibility of erroneous responder act in dispatching the rescue team from the nearest hospital. A new assistive intelligent system is designed in this regard that includes both software and hardware units. Hardware unit is used as an On-Board Unit (OBU), which consists of GPS, GPRS and gyroscope modules. Once OBU detects the accident, a notification system designed and connected to OBU will sent an alarm to the server. The distance to the nearest emergency center is calculated using Dijkstra algorithm. Then the server sends a request for assistance to the nearest emergency centre. The proposed system is developed and tested at local laboratory conditions. The results show that this system can reduce Ambulance Arrival Time (AAT). The preliminary results and architecture of the system have been presented. The inclination angle determined by the proposed system along with the car position identified by the installed GPS sensor assists the crash/accident warning part of the system to send a help request to the nearest road emergency centre. These results verified that the probability of having a remote and smart car crash/accident decision support system using the proposed system has been improved compared to that of the existing systems.

ACS Style

A. H. Nourbakhsh; M. R. Delavar; M. Jadidi; B. Moshiri. REDUCING THE TIME TO GET EMERGENCY ASSISTANCE FOR ACCIDENT VEHICLES ON THE ROAD THROUGH AN INTELLIGENT TRANSPORTATION SYSTEM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W18, 827 -832.

AMA Style

A. H. Nourbakhsh, M. R. Delavar, M. Jadidi, B. Moshiri. REDUCING THE TIME TO GET EMERGENCY ASSISTANCE FOR ACCIDENT VEHICLES ON THE ROAD THROUGH AN INTELLIGENT TRANSPORTATION SYSTEM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W18 ():827-832.

Chicago/Turabian Style

A. H. Nourbakhsh; M. R. Delavar; M. Jadidi; B. Moshiri. 2019. "REDUCING THE TIME TO GET EMERGENCY ASSISTANCE FOR ACCIDENT VEHICLES ON THE ROAD THROUGH AN INTELLIGENT TRANSPORTATION SYSTEM." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18, no. : 827-832.

Reviews
Published: 02 October 2019 in Geocarto International
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Urban sprawl is recognized as a challenge in urbanization. It is an unplanned spread of cities into its surrounding area that is caused by rising urban population. This phenomenon, if not properly controlled, can lead to several issues, e.g. traffic congestion, water and air pollution and excessive fuel consumption. A recent extension of the Shannon entropy such as the spatial entropy can be used to model urban sprawl. We introduce a new measure of spatial entropy from a previous measure of spatial entropy. The proposed spatial entropy adds the compositional information of land use maps to the spatial entropy. In order to consider affecting factors in urban sprawl calculations, a conditional spatial entropy is suggested. The urban growth process is affected by various environmental and socio-economic parameters. Some of these parameters have such a low significance in urban growth process, and then feature selection have some advantageous like to reduce overall training times, to deal with overfitting and to increase generalizability. Fuzzy rough set theory is utilized as a feature selection method. The results show that the proposed model provides some valuable evaluation of the urban sprawl as compared to previous methods. Moreover, the feature selection has a minute impact on the entropy values, especially in the new modified entropy, this implying to remove unimportant features from the features.

ACS Style

Davoud Parvinnezhad; Mahmoud Reza Delavar; Christophe Claramunt; Bryan C. Pijanowski. A modified spatial entropy for urban sprawl assessment. Geocarto International 2019, 36, 1804 -1819.

AMA Style

Davoud Parvinnezhad, Mahmoud Reza Delavar, Christophe Claramunt, Bryan C. Pijanowski. A modified spatial entropy for urban sprawl assessment. Geocarto International. 2019; 36 (16):1804-1819.

Chicago/Turabian Style

Davoud Parvinnezhad; Mahmoud Reza Delavar; Christophe Claramunt; Bryan C. Pijanowski. 2019. "A modified spatial entropy for urban sprawl assessment." Geocarto International 36, no. 16: 1804-1819.

Articles
Published: 28 June 2019 in Zoology in the Middle East
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We investigated the simultaneous and sympatric movements of a coalition of two Asiatic Cheetahs (Acinonyx jubatus venaticus) and a Persian Leopard (Panthera pardus saxicolor), two rare and highly mobile large felids in Bafq Protected Area, Iran. The animals were tracked with GPS collars for 4.5 to 9 months at a temporal resolution of eight hours. The cheetahs used lower elevations areas (average: 1600 m), and remained more distant to the surrounding highways of (average: 14.5 km) than the leopard (average: 1.8 km and 12.3 km, respectively). The leopard’s home range (408 km2) was almost entirely within the larger home ranges of the cheetah coalition (1,137 km2). We found that the leopard approached more closely to either of the cheetahs in the rare occasions when they were separated, though whether that was the response of the cheetahs to the leopard or vice versa is unknown. This interaction eventually culminated in the leopard killing one of the cheetahs, the first documented proof of lethal competition between cheetah and leopard in Iran. The combined risks of larger home ranges beyond the protected areas with higher probability of encounters with humans, of highway crossing, and predation by Persian Leopards contribute to the particularly precarious situation of the Asiatic Cheetah.

ACS Style

Farid Cheraghi; Mahmoud Reza Delavar; Farshad Amiraslani; Kazem Alavipanah; Eliezer Gurarie; Houman Jowkar; Luke Hunter; Stephane Ostrowski; William F. Fagan. Inter-dependent movements of Asiatic Cheetahs Acinonyx jubatus venaticus and a Persian Leopard Panthera pardus saxicolor in a desert environment in Iran (Mammalia: Felidae). Zoology in the Middle East 2019, 65, 283 -292.

AMA Style

Farid Cheraghi, Mahmoud Reza Delavar, Farshad Amiraslani, Kazem Alavipanah, Eliezer Gurarie, Houman Jowkar, Luke Hunter, Stephane Ostrowski, William F. Fagan. Inter-dependent movements of Asiatic Cheetahs Acinonyx jubatus venaticus and a Persian Leopard Panthera pardus saxicolor in a desert environment in Iran (Mammalia: Felidae). Zoology in the Middle East. 2019; 65 (4):283-292.

Chicago/Turabian Style

Farid Cheraghi; Mahmoud Reza Delavar; Farshad Amiraslani; Kazem Alavipanah; Eliezer Gurarie; Houman Jowkar; Luke Hunter; Stephane Ostrowski; William F. Fagan. 2019. "Inter-dependent movements of Asiatic Cheetahs Acinonyx jubatus venaticus and a Persian Leopard Panthera pardus saxicolor in a desert environment in Iran (Mammalia: Felidae)." Zoology in the Middle East 65, no. 4: 283-292.

Articles
Published: 24 June 2019 in Geocarto International
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One of the most important reasons for the existence of geologic risk during the hydrocarbon exploration process is related to uncertainties in geospatial data and models employed for data fusion. This study proposes a geospatial information system (GIS)-assisted approach integrated with soft computing methods to manage spatial uncertainties during the hydrocarbon exploration process. A framework was designed to illustrate the process of calculating the geologic risk interval of each hydrocarbon structure and its estimation of uncertainties. The model enhances the geologic risk analysis of a Dempster–Shafer data-driven method by a fuzzy logic approach. The resultant hybrid method showed high predictive power with the area under the success and predictive curves being 82.2% and 75.9%, respectively. According to the results, the proposed hybrid method has improved the quality of risk analysis.

ACS Style

Sahand Seraj; Mahmoud Reza Delavar; Reza Rezaee. A hybrid GIS-assisted framework to integrate Dempster–Shafer theory of evidence and fuzzy sets in risk analysis: an application in hydrocarbon exploration. Geocarto International 2019, 36, 820 -838.

AMA Style

Sahand Seraj, Mahmoud Reza Delavar, Reza Rezaee. A hybrid GIS-assisted framework to integrate Dempster–Shafer theory of evidence and fuzzy sets in risk analysis: an application in hydrocarbon exploration. Geocarto International. 2019; 36 (7):820-838.

Chicago/Turabian Style

Sahand Seraj; Mahmoud Reza Delavar; Reza Rezaee. 2019. "A hybrid GIS-assisted framework to integrate Dempster–Shafer theory of evidence and fuzzy sets in risk analysis: an application in hydrocarbon exploration." Geocarto International 36, no. 7: 820-838.

Journal article
Published: 23 February 2019 in ISPRS International Journal of Geo-Information
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Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents have long been struggling with air pollution damage such as the health issues of its citizens. As far as the study area of this research is concerned, a considerable proportion of Tehran air pollution is attributed to PM10 and PM2.5 pollutants. Therefore, the present study was conducted to determine the prediction models to determine air pollutions based on PM10 and PM2.5 pollution concentrations in Tehran. To predict the air-pollution, the data related to day of week, month of year, topography, meteorology, and pollutant rate of two nearest neighbors as the input parameters and machine learning methods were used. These methods include a regression support vector machine, geographically weighted regression, artificial neural network and auto-regressive nonlinear neural network with an external input as the machine learning method for the air pollution prediction. A prediction model was then proposed to improve the afore-mentioned methods, by which the error percentage has been reduced and improved by 57%, 47%, 47% and 94%, respectively. The most reliable algorithm for the prediction of air pollution was autoregressive nonlinear neural network with external input using the proposed prediction model, where its one-day prediction error reached 1.79 µg/m3. Finally, using genetic algorithm, data for day of week, month of year, topography, wind direction, maximum temperature and pollutant rate of the two nearest neighbors were identified as the most effective parameters in the prediction of air pollution.

ACS Style

Mahmoud Reza Delavar; Amin Gholami; Gholam Reza Shiran; Yousef Rashidi; Gholam Reza Nakhaeizadeh; Kurt Fedra; Smaeil Hatefi Afshar. A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran. ISPRS International Journal of Geo-Information 2019, 8, 99 .

AMA Style

Mahmoud Reza Delavar, Amin Gholami, Gholam Reza Shiran, Yousef Rashidi, Gholam Reza Nakhaeizadeh, Kurt Fedra, Smaeil Hatefi Afshar. A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran. ISPRS International Journal of Geo-Information. 2019; 8 (2):99.

Chicago/Turabian Style

Mahmoud Reza Delavar; Amin Gholami; Gholam Reza Shiran; Yousef Rashidi; Gholam Reza Nakhaeizadeh; Kurt Fedra; Smaeil Hatefi Afshar. 2019. "A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran." ISPRS International Journal of Geo-Information 8, no. 2: 99.

Journal article
Published: 30 April 2018 in International Journal of Ambient Energy
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ACS Style

Isham Alzoubi; Mahmoud R. Delavar; Farhad Mirzaei; Babak Nadjar Arrabi. Effect of soil properties for prediction of energy consumption in land levelling irrigation. International Journal of Ambient Energy 2018, 41, 475 -488.

AMA Style

Isham Alzoubi, Mahmoud R. Delavar, Farhad Mirzaei, Babak Nadjar Arrabi. Effect of soil properties for prediction of energy consumption in land levelling irrigation. International Journal of Ambient Energy. 2018; 41 (4):475-488.

Chicago/Turabian Style

Isham Alzoubi; Mahmoud R. Delavar; Farhad Mirzaei; Babak Nadjar Arrabi. 2018. "Effect of soil properties for prediction of energy consumption in land levelling irrigation." International Journal of Ambient Energy 41, no. 4: 475-488.

Articles
Published: 09 October 2017 in Geosystem Engineering
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Land leveling is one of the most important steps in soil preparation for consequent objectives. Parallel policies need to take both energy and environmental subjects into the account as well as certain financial development and eco-friendly protection. The objective of this research was to develop the five methods of GA-ANN, ICA-ANN, PSO-ANN, sensitivity analysis, and ANFIS to predict the environmental indicators for land leveling. In this study, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand percent, and soil swelling index were investigated that are the main affecting parameters in energy consumption through land leveling. A total of 90 samples were prepared from three land areas. Acquired data were used to develop accurate models for Labor, LE (Labor Energy), FE (Fuel Energy), TMC (Total Machinery Cost), and TME (Total Machinery Energy). Results of sensitivity analysis showed that only three parameters of soil compressibility, density of soil, and cut/fill volume had significant effects on energy consumption. The results showed among the mentioned methods for estimating the amount of energy required in different parts such as labor, fuel, and machinery, GA-ANN and ICA-ANN methods were more precise than others. The sensitivity analysis method was the least accurate. Finally, it was concluded that the GA-ANN method was the best due to its high R2 and low RMSE values.

ACS Style

Isham Alzoubi; Mahmoud R. Delavar; Farhad Mirzaei; Babak Nadjar Araabi. Comparing ANFIS and integrating algorithm models (ICA-ANN, PSO-ANN, and GA-ANN) for prediction of energy consumption for irrigation land leveling. Geosystem Engineering 2017, 21, 81 -94.

AMA Style

Isham Alzoubi, Mahmoud R. Delavar, Farhad Mirzaei, Babak Nadjar Araabi. Comparing ANFIS and integrating algorithm models (ICA-ANN, PSO-ANN, and GA-ANN) for prediction of energy consumption for irrigation land leveling. Geosystem Engineering. 2017; 21 (2):81-94.

Chicago/Turabian Style

Isham Alzoubi; Mahmoud R. Delavar; Farhad Mirzaei; Babak Nadjar Araabi. 2017. "Comparing ANFIS and integrating algorithm models (ICA-ANN, PSO-ANN, and GA-ANN) for prediction of energy consumption for irrigation land leveling." Geosystem Engineering 21, no. 2: 81-94.

Journal article
Published: 27 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Fuel consumption has significantly increased due to the growth of the population. A solution to address this problem is the underground storage of natural gas. The first step to reach this goal is to select suitable places for the storage. In this study, site selection for the underground natural gas reservoirs has been performed using a multi-criteria decision-making in a GIS environment. The “Ordered Weighted Average” (OWA) operator is one of the multi-criteria decision-making methods for ranking the criteria and consideration of uncertainty in the interaction among the criteria. In this paper, Fuzzy AHP_OWA (FAHP_OWA) is used to determine optimal sites for the underground natural gas reservoirs. Fuzzy AHP_OWA considers the decision maker’s risk taking and risk aversion during the decision-making process. Gas consumption rate, temperature, distance from main transportation network, distance from gas production centers, population density and distance from gas distribution networks are the criteria used in this research. Results show that the northeast and west of Iran and the areas around Tehran (Tehran and Alborz Provinces) have a higher attraction for constructing a natural gas reservoir. The performance of the used method was also evaluated. This evaluation was performed using the location of the existing natural gas reservoirs in the country and the site selection maps for each of the quantifiers. It is verified that the method used in this study is capable of modeling different decision-making strategies used by the decision maker with about 88 percent of agreement between the modeling and test data.

ACS Style

A. R. Sabzevari; M. R. Delavar. GIS-BASED SITE SELECTION FOR UNDERGROUND NATURAL RESOURCES USING FUZZY AHP-OWA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-4/W4, 463 -468.

AMA Style

A. R. Sabzevari, M. R. Delavar. GIS-BASED SITE SELECTION FOR UNDERGROUND NATURAL RESOURCES USING FUZZY AHP-OWA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-4/W4 ():463-468.

Chicago/Turabian Style

A. R. Sabzevari; M. R. Delavar. 2017. "GIS-BASED SITE SELECTION FOR UNDERGROUND NATURAL RESOURCES USING FUZZY AHP-OWA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4, no. : 463-468.

Journal article
Published: 26 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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By and large, todays mega cities are confronting considerable urban development in which many new buildings are being constructed in fringe areas of these cities. This remarkable urban development will probably end in vegetation reduction even though each mega city requires adequate areas of vegetation, which is considered to be crucial and helpful for these cities from a wide variety of perspectives such as air pollution reduction, soil erosion prevention, and eco system as well as environmental protection. One of the optimum methods for monitoring this vital component of each city is multi-temporal satellite images acquisition and using change detection techniques. In this research, the vegetation and urban changes of Mashhad, Iran, were monitored using an object-oriented (marker-based watershed algorithm) post classification comparison (PCC) method. A Bi-temporal multi-spectral Landsat satellite image was used from the study area to detect the changes of urban and vegetation areas and to find a relation between these changes. The results of this research demonstrate that during 1987-2017, Mashhad urban area has increased about 22525 hectares and the vegetation area has decreased approximately 4903 hectares. These statistics substantiate the close relationship between urban development and vegetation reduction. Moreover, the overall accuracies of 85.5% and 91.2% were achieved for the first and the second image classification, respectively. In addition, the overall accuracy and kappa coefficient of change detection were assessed 84.1% and 70.3%, respectively.

ACS Style

N. Khalili Moghadam; M. R. Delavar; A. Forati. VEGETATION MONITORING OF MASHHAD USING AN OBJECT-ORIENTED POST CLASSIFICATION COMPARISON METHOD. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-4/W4, 123 -131.

AMA Style

N. Khalili Moghadam, M. R. Delavar, A. Forati. VEGETATION MONITORING OF MASHHAD USING AN OBJECT-ORIENTED POST CLASSIFICATION COMPARISON METHOD. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-4/W4 ():123-131.

Chicago/Turabian Style

N. Khalili Moghadam; M. R. Delavar; A. Forati. 2017. "VEGETATION MONITORING OF MASHHAD USING AN OBJECT-ORIENTED POST CLASSIFICATION COMPARISON METHOD." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4, no. : 123-131.

Journal article
Published: 14 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Disaster risk is a function of hazard and vulnerability. Risk is defined as the expected losses, including lives, personal injuries, property damages, and economic disruptions, due to a particular hazard for a given area and time period. Risk assessment is one of the key elements of a natural disaster management strategy as it allows for better disaster mitigation and preparation. It provides input for informed decision making, and increases risk awareness among decision makers and other stakeholders. Virtual globes such as Google Earth can be used as a visualization tool. Proper spatiotemporal graphical representations of the concerned risk significantly reduces the amount of effort to visualize the impact of the risk and improves the efficiency of the decision-making process to mitigate the impact of the risk. The spatiotemporal visualization of tsunami waves for disaster management process is an attractive topic in geosciences to assist investigation of areas at tsunami risk. In this paper, a method for coupling virtual globes with tsunami wave arrival time models is presented. In this process we have shown 2D+Time of tsunami waves for propagation and inundation of tsunami waves, both coastal line deformation, and the flooded areas. In addition, the worst case scenario of tsunami on Chabahar port derived from tsunami modelling is also presented using KML on google earth.

ACS Style

H. Mohammadi; M. R. Delavar; M. A. Sharifi; M. D. Pirooz. SPATIOTEMPORAL VISUALIZATION OF TSUNAMI WAVES USING KML ON GOOGLE EARTH. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W7, 1291 -1299.

AMA Style

H. Mohammadi, M. R. Delavar, M. A. Sharifi, M. D. Pirooz. SPATIOTEMPORAL VISUALIZATION OF TSUNAMI WAVES USING KML ON GOOGLE EARTH. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W7 ():1291-1299.

Chicago/Turabian Style

H. Mohammadi; M. R. Delavar; M. A. Sharifi; M. D. Pirooz. 2017. "SPATIOTEMPORAL VISUALIZATION OF TSUNAMI WAVES USING KML ON GOOGLE EARTH." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7, no. : 1291-1299.

Journal article
Published: 12 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The problem of overcrowding of mega cities has been bolded in recent years. To meet the need of housing this increased population, which is of great importance in mega cities, a huge number of buildings are constructed annually. With the ever-increasing trend of building constructions, we are faced with the growing trend of building infractions and illegal buildings (IBs). Acquiring multi-temporal satellite images and using change detection techniques is one of the proper methods of IB monitoring. Using the type of satellite images with different spatial and spectral resolutions has always been an issue in efficient detection of the building changes. In this research, three bi-temporal high-resolution satellite images of IRS-P5, GeoEye-1 and QuickBird sensors acquired from the west of metropolitan area of Tehran, capital of Iran, in addition to city maps and municipality property database were used to detect the under construction buildings with improved performance and accuracy. Furthermore, determining the employed bi-temporal satellite images to provide better performance and accuracy in the case of IB detection is the other purpose of this research. The Kappa coefficients of 70 %, 64 %, and 68 % were obtained for producing change image maps using GeoEye-1, IRS-P5, and QuickBird satellite images, respectively. In addition, the overall accuracies of 100 %, 6 %, and 83 % were achieved for IB detection using the satellite images, respectively. These accuracies substantiate the fact that the GeoEye-1 satellite images had the best performance among the employed images in producing change image map and detecting the IBs.

ACS Style

N. Khalilimoghadama; M. R. Delavar; P. Hanachi. PERFORMANCE EVALUATION OF THREE DIFFERENT HIGH RESOLUTION SATELLITE IMAGES IN SEMI-AUTOMATIC URBAN ILLEGAL BUILDING DETECTION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W7, 505 -514.

AMA Style

N. Khalilimoghadama, M. R. Delavar, P. Hanachi. PERFORMANCE EVALUATION OF THREE DIFFERENT HIGH RESOLUTION SATELLITE IMAGES IN SEMI-AUTOMATIC URBAN ILLEGAL BUILDING DETECTION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W7 ():505-514.

Chicago/Turabian Style

N. Khalilimoghadama; M. R. Delavar; P. Hanachi. 2017. "PERFORMANCE EVALUATION OF THREE DIFFERENT HIGH RESOLUTION SATELLITE IMAGES IN SEMI-AUTOMATIC URBAN ILLEGAL BUILDING DETECTION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7, no. : 505-514.

Journal article
Published: 12 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Several faults exist in the vicinity of Tehran, the capital of Iran such as North Tehran, Ray, Mosha and Kahrizak. One way to assist reducing the damage caused by the earthquake is the production of a seismic vulnerability map. The study area in this research is Tehran, based on the assumption of the activation of North Tehran fault. Degree of Physical seismic vulnerability caused by the earthquake depends on a number of criteria. In this study the intensity of the earthquake, land slope, numbers of buildings’ floors as well as their materials are considered as the effective parameters. Hence, the production of the seismic vulnerability map is a multi criteria issue. In this problem, the main source of uncertainty is related to the experts’ opinions regarding the seismic vulnerability of Tehran statistical units. The main objectives of this study are to exploit opinions of the experts, undertaking interval computation and interval Dempster-Shafer combination rule to reduce the uncertainty in the opinions of the experts and customizing granular computing to extract the rules and to produce Tehran physical seismic vulnerability map with a higher confidence. Among 3174 statistical units of Tehran, 150 units were randomly selected and using interval computation, their physical vulnerabilities were determined by the experts in earthquake-related fields. After the fusion of the experts’ opinions using interval Dempster-Shafer, the information table is prepared as the input to granular computing and then rules are extracted with minimum inconsistency. Finally, the seismic physical vulnerability map of Tehran was produced with % 72 accuracy.

ACS Style

M. R. Delavar; M. Bahrami; M. Zare. PHYSICAL SEISMIC VULNERABILITY ASSESSMENT OF TEHRAN USING THE INTEGRATION OF GRANULAR COMPUTING AND INTERVAL DEMPSTER- SHAFER. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W7, 469 -477.

AMA Style

M. R. Delavar, M. Bahrami, M. Zare. PHYSICAL SEISMIC VULNERABILITY ASSESSMENT OF TEHRAN USING THE INTEGRATION OF GRANULAR COMPUTING AND INTERVAL DEMPSTER- SHAFER. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W7 ():469-477.

Chicago/Turabian Style

M. R. Delavar; M. Bahrami; M. Zare. 2017. "PHYSICAL SEISMIC VULNERABILITY ASSESSMENT OF TEHRAN USING THE INTEGRATION OF GRANULAR COMPUTING AND INTERVAL DEMPSTER- SHAFER." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7, no. : 469-477.

Journal article
Published: 12 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The well-known historical tsunami in the Makran Subduction Zone (MSZ) region was generated by the earthquake of November 28, 1945 in Makran Coast in the North of Oman Sea. This destructive tsunami killed over 4,000 people in Southern Pakistan and India, caused great loss of life and devastation along the coasts of Western India, Iran and Oman. According to the report of "Remembering the 1945 Makran Tsunami", compiled by the Intergovernmental Oceanographic Commission (UNESCO/IOC), the maximum inundation of Chabahar port was 367 m toward the dry land, which had a height of 3.6 meters from the sea level. In addition, the maximum amount of inundation at Pasni (Pakistan) reached to 3 km from the coastline. For the two beaches of Gujarat (India) and Oman the maximum run-up height was 3 m from the sea level. In this paper, we first use Makran 1945 seismic parameters to simulate the tsunami in generation, propagation and inundation phases. The effect of tsunami on Chabahar port is simulated using the ComMIT model which is based on the Method of Splitting Tsunami (MOST). In this process the results are compared with the documented eyewitnesses and some reports from researchers for calibration and validation of the result. Next we have used the model to perform risk assessment for Chabahar port in the south of Iran with the worst case scenario of the tsunami. The simulated results showed that the tsunami waves will reach Chabahar coastline 11 minutes after generation and 9 minutes later, over 9.4 Km2 of the dry land will be flooded with maximum wave amplitude reaching up to 30 meters.

ACS Style

M. R. Delavar; H. Mohammadi; M. A. Sharifi; M. D. Pirooz. TSUNAMI RISK ASSESSMENT MODELLING IN CHABAHAR PORT, IRAN. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W7, 461 -467.

AMA Style

M. R. Delavar, H. Mohammadi, M. A. Sharifi, M. D. Pirooz. TSUNAMI RISK ASSESSMENT MODELLING IN CHABAHAR PORT, IRAN. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W7 ():461-467.

Chicago/Turabian Style

M. R. Delavar; H. Mohammadi; M. A. Sharifi; M. D. Pirooz. 2017. "TSUNAMI RISK ASSESSMENT MODELLING IN CHABAHAR PORT, IRAN." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7, no. : 461-467.

Journal article
Published: 24 August 2017 in Journal of Intelligent & Fuzzy Systems
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Mansoureh Sadrykia; Mahmoud Reza Delavar; Mehdi Zare. A GIS-based decision making model using fuzzy sets and theory of evidence for seismic vulnerability assessment under uncertainty (case study: Tabriz). Journal of Intelligent & Fuzzy Systems 2017, 33, 1969 -1981.

AMA Style

Mansoureh Sadrykia, Mahmoud Reza Delavar, Mehdi Zare. A GIS-based decision making model using fuzzy sets and theory of evidence for seismic vulnerability assessment under uncertainty (case study: Tabriz). Journal of Intelligent & Fuzzy Systems. 2017; 33 (3):1969-1981.

Chicago/Turabian Style

Mansoureh Sadrykia; Mahmoud Reza Delavar; Mehdi Zare. 2017. "A GIS-based decision making model using fuzzy sets and theory of evidence for seismic vulnerability assessment under uncertainty (case study: Tabriz)." Journal of Intelligent & Fuzzy Systems 33, no. 3: 1969-1981.

Journal article
Published: 14 May 2017 in Transactions in GIS
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This study proposes multi-criteria group decision-making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi-layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty-five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R2 = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.

ACS Style

Hossein Sheikhian; Mahmoud Reza Delavar; Alfred Stein. A GIS-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks. Transactions in GIS 2017, 21, 1237 -1259.

AMA Style

Hossein Sheikhian, Mahmoud Reza Delavar, Alfred Stein. A GIS-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks. Transactions in GIS. 2017; 21 (6):1237-1259.

Chicago/Turabian Style

Hossein Sheikhian; Mahmoud Reza Delavar; Alfred Stein. 2017. "A GIS-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks." Transactions in GIS 21, no. 6: 1237-1259.

Journal article
Published: 14 April 2017 in ISPRS International Journal of Geo-Information
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Earthquakes are one of the natural disasters that threaten many lives every year. It is important to estimate seismic damages in advance to be able to reduce future losses. However, seismic vulnerability assessment is a complicated problem, especially in areas with incomplete data, due to incorporated uncertainties. Therefore, it is important to use adequate methods that take into account and handle the associated uncertainties. Although different seismic vulnerability assessment methods at the urban scale have been proposed, the purpose of this research is to introduce a new Geospatial Information System GIS-based model using a modified integration of Analytical Hierarchy Process (AHP), fuzzy sets theory, and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in a vector-based environment. The proposed method emphasizes handling one of the important uncertainties in areas with incomplete data, namely the ‘vagueness’ of the existing knowledge about influences of the criteria on seismic vulnerability, which is handled using fuzzy sets theory in this research. The applicability of the proposed method is tested in a municipality district of Tabriz, which is in a near vicinity to the fault system. It can be concluded that the proposed method contributes to a pragmatic and efficient assessment of physical seismic vulnerability under uncertainty, which provides useful information for assisting planners in mitigation and preparation stages in less-studied areas.

ACS Style

Mansoureh Sadrykia; Mahmoud Reza Delavar; Mehdi Zare. A GIS-Based Fuzzy Decision Making Model for Seismic Vulnerability Assessment in Areas with Incomplete Data. ISPRS International Journal of Geo-Information 2017, 6, 119 .

AMA Style

Mansoureh Sadrykia, Mahmoud Reza Delavar, Mehdi Zare. A GIS-Based Fuzzy Decision Making Model for Seismic Vulnerability Assessment in Areas with Incomplete Data. ISPRS International Journal of Geo-Information. 2017; 6 (4):119.

Chicago/Turabian Style

Mansoureh Sadrykia; Mahmoud Reza Delavar; Mehdi Zare. 2017. "A GIS-Based Fuzzy Decision Making Model for Seismic Vulnerability Assessment in Areas with Incomplete Data." ISPRS International Journal of Geo-Information 6, no. 4: 119.

Journal article
Published: 15 December 2016 in ISPRS International Journal of Geo-Information
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This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error) of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was confirmed.

ACS Style

Fereydoun Naghibi; Mahmoud Reza Delavar. Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling. ISPRS International Journal of Geo-Information 2016, 5, 241 .

AMA Style

Fereydoun Naghibi, Mahmoud Reza Delavar. Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling. ISPRS International Journal of Geo-Information. 2016; 5 (12):241.

Chicago/Turabian Style

Fereydoun Naghibi; Mahmoud Reza Delavar. 2016. "Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling." ISPRS International Journal of Geo-Information 5, no. 12: 241.