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Öğe A GIS-based AHP approach for emergency warehouse site selection: A case close to Turkey-Syria border(Academic Publication Council, 2022) Cetinkaya, Cihan; Ozceylan, Eren; Keser, IlhanAlthough the term disaster includes natural events like earthquake, flood, and drought, it also covers the wars, intense migration waves, industrial accidents, and even epidemic diseases. In recent years, the number and severity of both natural and man-made disasters has been increasing. In this context Gaziantep-the border city of Turkey to Syria-is facing many logistical problems because of the crisis in the region that has a broad repercussion in press. In addition, the coronavirus pandemic increased the supply traffic in the region. The region is in need for many emergency warehouses to store the emergency supplies and send to the needy. Thus, a three-step hybrid solution method is developed to solve this real life problem. The first stage is the determination of selection criteria; secondly the spatial database is created by using a Geographical Information System (GIS). Then, Analytic Hierarchy Process (AHP) technique is applied to assign the importance levels to the selection criteria to generate the suitability map to choose the most appropriate emergency warehouse site selection in Gaziantep. Additionally, scenario analyses are conducted to understand the effects of importance levels on the problem results. As a result, 1.3% of the study area is determined as quite suitable for establishing an emergency warehouse.Öğe A GIS-based DANP-VIKOR approach to evaluate R&D performance of Turkish cities(Emerald Group Publishing Ltd, 2019) Ozkan, Baris; Ozceylan, Eren; Korkmaz, Ibrahim Halil; Cetinkaya, CihanPurpose The purpose of this study is to measure the R&D performance of 81 cities in Turkey by using a scientific approach. Design/methodology/approach A four-step solution approach is developed for this problem. In the first step, a hierarchical structure of 14 indicators (including number of patents, publications, R&D expense, etc.) in three dimensions is constructed. In the second step, explicitly and implicitly spatial indicators such as university location and R&D manpower are mapped by using geographic information system (GIS). In the third step, a hybrid multi-criteria decision making model, namely, DANP that combines decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (ANP) techniques is applied to assign different level of importance to the indicators. In the last step, Visekriterijumska Optimizacijai kompromisno Resenje (VIKOR) method is used to rank the performance of 81 cities. Obtained results are visualized using GIS to show the pros and cons of each city in terms of R&D performance. Findings Results of the paper show that Istanbul, Ankara and Konya are ordered as contenders of best R&D performances and on the contrary, Igdir, Sirnak and Tunceli are ordered as the worst R&D performances among 81 cities. Research limitations/implications - One limitation of the study can be the considered criteria. However, all the criteria are obtained from literature and experts; thus, the paper covers as much criteria as possible. Practical implications - The proposed study may allow Ministry of Science, Industry and Technology of Turkey to formulate more effective strategies to improve cities' R&D performance. In addition, any country can apply the same methodology for measuring the R&D performance of their cities by using their related data. As the worst R&D city performances belong to the eastern part of Turkey, it can be deducted that the socio-cultural structure of the eastern part of the country needs improvement. Originality/value To the best of the author's knowledge, this is the first study which applies a GIS-based MCDM approach for R&D performance measurement. Thus, the value of this paper belongs to both literature and real life.Öğe A GIS-based MCDM approach for the evaluation of bike-share stations(Elsevier Sci Ltd, 2018) Kabak, Mehmet; Erbas, Mehmet; Cetinkaya, Cihan; Ozceylan, Eren-Several benefits have contributed to the increasing popularity of bike-share systems in cities around the world. In addition to traffic congestion, environmental concerns are also compelling cities to seek more sustainable modes of transportation. A key factor in the efficacy of bike-share networks is the location of bike stations in relation to potential related criteria. Therefore, site suitability analysis for bike-share stations using quantitative methods is essential. This study attempted to evaluate the current status of bike-share stations in Karsiyaka, Izmir, and to locate future station sites by comparing them to existing stations. To do so, different multi-criteria decision-making methods were combined with a geographic information system (GIS) to address twelve conflicting criteria. Specifically, the analytic hierarchy process was applied to obtain criteria weights, and multi-objective optimization by ratio analysis was used to evaluate current and potential alternatives. Our study demonstrates the superiority of the suggested locations compared to the existing stations. (C) 2018 Elsevier Ltd. All rights reserved.Öğe A Hybrid Model Based on FAHP and WASPAS for Evaluation of Explosive and Narcotics Trace Detection Devices(Springer Science and Business Media Deutschland GmbH, 2022) Ozceylan, Eren; Ozkan, Baris; Cetinkaya, CihanAs a result of Industry 4.0, the aviation industry is started to be re-designed with cyber–physical systems, Internet of Things, cloud computing and similar technologies. In Aviation 4.0, where cyber–physical systems are used to assist humans’ unkind work and to complete tasks autonomously, security applications are one of the promising and tight operations that need to be considered. The detection of explosives and narcotics material for the purposes of aviation security is an important area for preventing terrorism and smuggling. For this purpose, there are some novel technologies (devices) in the framework of Aviation 4.0. Evaluation and selection the best device is a multi-criteria decision making (MCDM) problem which includes ambiguities and vagueness. To do so, fuzzy analytic hierarchy process (FAHP) is applied for assigning weights of the attributes and weighted aggregated sum product assessment (WASPAS) method is used to determine the most suitable alternative device for explosive and narcotics trace detection. As a result, three well-known devices in the market are evaluated and the best alternative is suggested. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe A Logistics Performance Index Review: A Glance at the World's Best and Worst Ten Performances(Springer International Publishing Ag, 2021) Cetinkaya, Cihan; Ozceylan, ErenTransport and logistics have a very significant place in international trade. Thus, the measurement of logistics performance is directly related with a country's trade income. The Logistics Performance Index (LPI) was created by the World Bank to measure the performance of countries in the field of logistics. The index is created after asking a number of categorized questions to employees and managers of several logistics companies located in each country and determining the results with respect to the question scores. Although it is not the only determiner in this sector, the LPI is very important because the supply chain members use this index as a decision making tool. The index is released biennially and there are 6 reports between 2007 and 2018. In this paper, 6 Logistics Performance Reports are analyzed; the top 10 and worst 10 performances are criticized. Also Turkey's overall performance is examined and discussed specifically. The intersection countries are highlighted and the reasons are discussed. It is determined that generally high income countries are performing well and the countries with internal disturbances are performing indifferently. It is believed that this research will be helpful to understand the global picture of logistics activities.Öğe A mass vaccination site selection problem: An application of GIS and entropy-based MAUT approach(Elsevier Science Inc, 2023) Cetinkaya, Cihan; Erbas, Mehmet; Kabak, Mehmet; Ozceylan, ErenCoronavirus disease (COVID-19) was recognized in December 2019 and spread very severely throughout the world. In 2022 May, the total death numbers reached 6.28 million people worldwide. During the pandemic, some alternative vaccines were discovered in the middle of 2020. Today, many countries are struggling to supply vaccines and vaccinate their citizens. Besides the difficulties of vaccine supply, mass vaccination is a challenging but mandatory task for the countries. Within this context, determining the mass vaccination site is very important for recovering, thus a five-step approach is generated in this paper to solve this real-life problem. Firstly the mass vaccination site selection criteria are determined, and secondly, the spatial data are collected and mapped by using Geographical Information System (GIS) software. Then, the entropy weighting method (EWM) is used for determining the relative importance levels of criteria and fourthly, the multiple attribute utility theory (MAUT) approach is used for ranking the potential mass vaccination sites. Lastly, ranked alternative sites are analyzed using network analyst tool of GIS in terms of covered population. A case study is conducted in Gaziantep city which is the ninth most population and having above-average COVID-19 patients in Turkey. As a result, the fourth alternative (around the S,ehitkamil Monument) is chosen as the best mass vaccination site for the city. It is believed that the outcomes of the paper could be used by city planners and decision-makers.Öğe A multi-criteria spatial analysis using GIS to evaluate potential sites for a new border gate on Turkey's Syria frontier(Inderscience Enterprises Ltd, 2020) Kabak, Mehmet; Ozceylan, Eren; Erbas, Mehmet; Cetinkaya, CihanAfter the internal disturbance in Syria in 2011, many Syrian refugees migrated to Turkey progressively, and the Turkish Government provided humanitarian aid to people in Syria. These incidents caused a huge amount of density on current border gates. Also, increasing potential terrorist attacks and growing frontier infringements also create a need for a new border gate on Turkey's Syria frontier. Thus, a four-step hybrid solution approach is developed for this problem. This approach starts with determination of selection criteria; then, the spatial database of these criteria is created by using a geographical information system. In the third step, the DEMATEL technique is applied to assign importance levels to the criteria. Lastly, MULTIMOORA technique is used to rank the potential sites. The results indicate that, recommended potential sites are more suitable than current border gates. This paper can serve as a scientific-base while selecting the optimal site for border gates. [Received: 8 February 2019; Revised: 1 July 2019; Accepted: 7 August 2019]Öğe A novel approach for text categorization by applying hybrid genetic bat algorithm through feature extraction and feature selection methods(Pergamon-Elsevier Science Ltd, 2022) Eliguzel, Nazmiye; Cetinkaya, Cihan; Dereli, TuerkayDue to the rapid incline in the number of documents along with social media usage, text categorization has become an important concept. There are tasks required to be fulfilled during the text categorization, such as extracting useful data from different perspectives, reducing the high feature space dimension, and improving effectiveness. In order to accomplish these tasks, feature selection, and feature extraction gain importance. This paper investigates how to solve feature selection and extraction problems. Also, this study aims to decide which topics are the focus of a document. Moreover, the Twitter data-set is utilized as a document and an Uncapacitated P-Median Problem (UPMP) is applied to make clustering. In this study, UPMP is used on Twitter data collection for the first time to collect clustered tweets. Therefore, a novel hybrid genetic bat algorithm (HGBA) is proposed to solve the UPMP for our case. The proposed novel approach is applied to analyze the Twitter data-set of the Nepal earthquake. The first part of the analysis includes the data pre-processing stage. The Latent Dirichlet Allocation (LDA) method is applied to the pre-processed text. After that, a similarity (distance) matrix is generated by utilizing the Jensen Shannon Divergence (JSD) model. The study's main goal is to use Twitter to assess the needs of victims during and after a disaster. To evaluate the applicability of the proposed approach, experiments are conducted on the OR-Library data-set. The results demonstrate that the proposed approach successfully extracts topics and categorizes text.Öğe A Risk-Based Location-Allocation Approach for Weapon Logistics(Mdpi, 2018) Cetinkaya, Cihan; Haffar, SamerGovernments have vital missions, such as securing their nation from many internal or external risks/threats. Thus, they prepare themselves against different scenarios. The most common scenario for all countries is facing attacks from other countries. However, training for these scenarios is not possible because military exercises are too expensive. The contribution of this paper is a scientific approach proposed for such a scenario. A mathematical model is developed to allocate different weapon types to a set of candidate locations (demand nodes, the military installations that need weapons) while minimizing total transportation costs, setup costs, and allocation risk. The risk arises from allocating the weapons to other military units as backups during a conflict. The risk increases when one military unit allocates their weapons to another unit during attacks. The mathematical model is tested on a case study problem of Turkish Land Forces. This case study is solved in 14 min, and the optimal total transportation and setup costs are determined. Since it is very important to make quick decisions during an attack, this scientific approach and computational time can be useful for military decision makers. Additionally, the results of this study can guarantee that any attack can be handled with the minimum cost and risk.Öğe A state-of-art optimization method for analyzing the tweets of earthquake-prone region(Springer London Ltd, 2021) Eliguzel, Nazmiye; Cetinkaya, Cihan; Dereli, TurkayWith the increase in accumulated data and usage of the Internet, social media such as Twitter has become a fundamental tool to access all kinds of information. Therefore, it can be expressed that processing, preparing data, and eliminating unnecessary information on Twitter gains its importance rapidly. In particular, it is very important to analyze the information and make it available in emergencies such as disasters. In the proposed study, an earthquake with the magnitude of Mw = 6.8 on the Richter scale that occurred on January 24, 2020, in Elazig province, Turkey, is analyzed in detail. Tweets under twelve hashtags are clustered separately by utilizing the Social Spider Optimization (SSO) algorithm with some modifications. The sum-of intra-cluster distances (SICD) is utilized to measure the performance of the proposed clustering algorithm. In addition, SICD, which works in a way of assigning a new solution to its nearest node, is used as an integer programming model to be solved with the GUROBI package program on the test data-sets. Optimal results are gathered and compared with the proposed SSO results. In the study, center tweets with optimal results are found by utilizing modified SSO. Moreover, results of the proposed SSO algorithm are compared with the K-means clustering technique which is the most popular clustering technique. The proposed SSO algorithm gives better results. Hereby, the general situation of society after an earthquake is deduced to provide moral and material supports.Öğe An eco-friendly evaluation for locating wheat processing plants: an integrated approach based on interval type-2 fuzzy AHP and COPRAS(Springer, 2022) Cetinkaya, Cihan; Ozkan, Baris; Ozceylan, Eren; Haffar, SamerSite selection for organizations is a complex and unstructured problem that must be analyzed carefully and properly since a localization error could drive to bankruptcy. This problem has been discussed widely and effectively using multi-criteria decision-making methods in a manufacturing context, but it has been little studied in the agricultural industry. The aim of this study is to proposing a methodological approach to evaluate the fifty states in the United States (US) in terms of wheat processing plant locations. To do so, a literature review is searched to determine the ten sub-criteria under financial, environmental, and spatial dimensions. To overcome the uncertainty of experts' judgment in criteria prioritization, interval type-2 fuzzy (IT2F)-analytic hierarchy process is used. The method of complex proportional assessment of alternatives is used to obtain the final rank of states. The results and verification of the methodology are carried out throughout a sensitivity analysis of the weights. The results of sensitivity analysis indicated that when financial aspects are focused upon, California is the best alternative, followed by Texas and Oklahoma. In addition, from the environmental and spatial perspectives, Vermont and California are the best choices, respectively. The findings of this study can provide useful information to wheat plant decision-makers and serve as a reference for US policy.Öğe Analysis of Potential High-Speed Rail Routes: A Case of GIS-Based Multicriteria Evaluation in Turkey(Asce-Amer Soc Civil Engineers, 2021) Ozceylan, Eren; Erbas, Mehmet; Cetinkaya, Cihan; Kabak, MehmetAll over the world, governments, policy makers, and practitioners are searching for proper routes and corridors to invest transportation infrastructures such as new railways, highways, and multimodal ways. Although it is one of the important steps of development, finding a suitable route for new transport infrastructure is a complicated and conflicting task for of various reasons. Possible social and environmental impacts on society and increasing cost and technical pressures on decision makers are some of these reasons. Taking into consideration aspects of different evaluation criteria, a geographic information system (GIS)-based multicriteria solution approach is proposed in this study. Potential high-speed rail (HSR) routes in Turkey are considered as a case study. After gathering and processing the related GIS data, weights are assigned to each criterion by using the fuzzy analytic hierarchy process in order to indicate their relative importance. Then, the additive ratio assessment method is applied to carry out the multicriteria (13 technical, social, and demographic criteria) evaluation and selection of the suitable alternatives (among 20 HSR routes) under given circumstances. It was found that the corridor from the west part of Turkey (from Izmir and Manisa) to the Marmara region (Kocaeli and Istanbul) had the highest priority, followed by the corridor from Ankara to Kayseri. HSR trains could potentially reduce the journey times to Kocaeli and Istanbul from Izmir and Manisa, as compared with driving, by 46% and 45%, respectively. The results of this study can be used to evaluate potential HSR corridors/routes or similar transport infrastructures in other countries.Öğe Application of named entity recognition on tweets during earthquake disaster: a deep learning-based approach(Springer, 2022) Eliguzel, Nazmiye; Cetinkaya, Cihan; Dereli, TurkayTwitter is an intensely utilized platform for disaster events and emergencies. Therefore, Twitter is an important resource for providing the essential information. Named entity recognition (NER), which is the process of determining the elementary units in a text and classifying them with pre-defined categories, plays a significant role to extract essential and usefulness information. However, NER is a challenging task due to the utilized informal text in the Twitter platform such as grammatical errors and nonstandard abbreviations. In this paper, recurrent neural network (RNN)-based approaches considering diversity of activation functions and optimization functions with NER tools are utilized to extract named entities such as organization, person, and location from the tweets. Inputs for RNN models are provided via two different NER tools which are natural language toolkit (NLTK) and general architecture for text engineering (Gate). Then, pre-labeled data are trained via GloVe word embedding technique, and RNN model variants such as LSTM, BLSTM, and GRU are demonstrated. Therefore, outperforming models among RNN variants are presented for predicting named entities. Yellowbrick interpreter is used for evaluation of the proposed method and Wilcoxon signed-rank test are applied on results of two different data sets to demonstrate consistency of the proposed method. In addition, comparison is made with existing machine learning methods. The experiments by utilizing the Nepal earthquake Twitter data set show that the RNN-based approaches achieve good results in finding named entities. In emergencies, the results of this paper can help in reducing the efforts of event location detection and provide better disaster management.Öğe Comparative analysis with topic modeling and word embedding methods after the Aegean Sea earthquake on Twitter(Springer Heidelberg, 2023) Eliguzel, Nazmiye; Cetinkaya, Cihan; Dereli, TurkayTopic detection from Twitter is a significant task that provides insight into real-time information. Recently, word embedding methods and topic modeling techniques have been utilized to find latent topics in various fields. Detecting topics leads to effective semantic structure and provides a better understanding of users. In the proposed study, different types of topic detection techniques are utilized, which are latent semantic analysis (LSA), Word2Vec, and latent Dirichlet allocation (LDA), and their performances are evaluated by the implementation of the K-means clustering technique on a real life application. In this case study, tweets were gathered after an earthquake with a magnitude of 6.6 on the Richter scale that took place on October 30, 2020, on the coast of the Aegean Sea (Izmir), Turkey. Tweets are clustered under fifteen hashtags separately, and the aforementioned techniques are applied to data-sets which vary in size. Therefore, the novelty of the proposed paper can be expressed as the comparison of different topic models and word embedding methods implemented for different sizes of documents in order to demonstrate the performance of these methods. While Word2Vec gives good results in small data-sets, LDA generally gives better results than Word2Vec and LSA in medium and large data-sets. Another aim of the proposed study is to provide information to decision makers for supporting victims and society. Therefore, the general situation of society is analyzed and society's attitude is demonstrated for decision-makers to take actionable activities such as psychological support, educational support, financial support, and political activities, etc.Öğe Comparative analysis with topic modeling and word embedding methods after the Aegean Sea earthquake on Twitter(Springer Heidelberg, 2023) Eliguzel, Nazmiye; Cetinkaya, Cihan; Dereli, TurkayTopic detection from Twitter is a significant task that provides insight into real-time information. Recently, word embedding methods and topic modeling techniques have been utilized to find latent topics in various fields. Detecting topics leads to effective semantic structure and provides a better understanding of users. In the proposed study, different types of topic detection techniques are utilized, which are latent semantic analysis (LSA), Word2Vec, and latent Dirichlet allocation (LDA), and their performances are evaluated by the implementation of the K-means clustering technique on a real life application. In this case study, tweets were gathered after an earthquake with a magnitude of 6.6 on the Richter scale that took place on October 30, 2020, on the coast of the Aegean Sea (Izmir), Turkey. Tweets are clustered under fifteen hashtags separately, and the aforementioned techniques are applied to data-sets which vary in size. Therefore, the novelty of the proposed paper can be expressed as the comparison of different topic models and word embedding methods implemented for different sizes of documents in order to demonstrate the performance of these methods. While Word2Vec gives good results in small data-sets, LDA generally gives better results than Word2Vec and LSA in medium and large data-sets. Another aim of the proposed study is to provide information to decision makers for supporting victims and society. Therefore, the general situation of society is analyzed and society's attitude is demonstrated for decision-makers to take actionable activities such as psychological support, educational support, financial support, and political activities, etc.Öğe Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study(Elsevier Sci Ltd, 2020) Eliguzel, Nazmiye; Cetinkaya, Cihan; Dereli, TurkayIn emergencies, Twitter is an important platform to get situational awareness simultaneously. Therefore, information about Twitter users' location is a fundamental aspect to understand the disaster effects. But location extraction is a challenging task. Most of the Twitter users do not share their locations in their tweets. In that respect, there are different methods proposed for location extraction which cover different fields such as statistics, machine learning, etc. This study is a sample study that utilizes geo-tagged tweets to demonstrate the importance of the location in disaster management by taking three cases into consideration. In our study, tweets are obtained by utilizing the earthquake keyword to determine the location of Twitter users. Tweets are evaluated by utilizing the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis through machine learning classification algorithms including the Multinomial and Gaussian Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Extra Trees, Neural Network, k Nearest Neighbor (kNN), Stochastic Gradient Descent (SGD), and Adaptive Boosting (AdaBoost) classifications. Therefore, 10 different machine learning algorithms are applied in our study by utilizing sentiment analysis based on location-specific disaster-related tweets by aiming fast and correct response in a disaster situation. In addition, the effectiveness of each algorithm is evaluated in order to gather the right machine learning algorithm. Moreover, topic extraction via LDA is provided to comprehend the situation after a disaster. The gathered results from the application of three cases indicate that Multinomial Naive Bayes and Extra Trees machine learning algorithms give the best results with an F-measure value over 80%. The study aims to provide a quick response to earthquakes by applying the aforementioned techniques.Öğe Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter(Springer, 2021) Dereli, Turkay; Eliguzel, Nazmiye; Cetinkaya, CihanIntensity of natural disasters has substantially increased; disaster management has gained importance along with this reason. In addition, social media has become an integral part of disaster management. Before, during and after disasters; people use social media and large number of output is obtained through social media activities. In this regard, Twitter is the most popular social media tool as micro blogging. Twitter has also become significant in complex disaster environment for coordinating events. It provides a swift way to collect crowd-sourced information. So, how do humanitarian organizations use Twitter platform? Humanitarian organizations utilize resources and related information while managing disasters. The effective use of social media by humanitarian agencies causes increased peoples' awareness. The international federation of red cross and Red Crescent Societies (IFRC) is the most significant humanitarian organization that aims providing assistance to people. Thus, the aim of this paper is to analyze IFRC's activities on Twitter and propose a perspective in the light of theoretical framework. Approximately, 5201 tweets are passed the pre-processing level, some important topics are extracted utilizing word labeling, latent dirichlet allocation (LDA model) and bag of Ngram model and sentiment analysis is applied based on machine learning classification algorithms including Naive Bayes, support vector machine SVM), decision tree, random forest, neural network and k-nearest neighbor (kNN) classifications. According to the classification accuracies, results demonstrate the superiority of support vector machine among other classification algorithms. This study shows us how IFRC uses Twitter and which topics IFRC emphasizes more.Öğe DESIGNING A FUZZY LOGIC CONTROLLER FOR A SINGLE INTERSECTION: A CASE STUDY IN GAZIANTEP(Yildiz Technical Univ, 2018) Dereli, Turkay; Cetinkaya, Cihan; Celik, NazmiyeThe traffic problem is a multidimensional problem and traffic lights are the key points to solving this problem. Thus, optimum green light duration helps reduce the traffic congestion. Queue length during red light and remaining vehicles in line after green light are important parameters for the determination of the green light duration. In this paper, a single intersection is taken into account with mentioned two inputs of fuzzy logic controller considering each intersection approach one by one. The output variables are chosen as phase selection and extend of green light duration. Unlike many other studies, these output variables are combined into one system based on modal distinction. Each approach is normalized according to the number of lanes to calculate a membership function value: because of different capacities of intersection approaches. The model is solved using MATLAB fuzzy inference system. The delay parameter is considered as performance measurement. Accordingly, proposed model is compared with various traditional models in literature. Finally, the model is evaluated using ANN (artificial neural network modeling) and ANFIS (adaptive neuro-fuzzy inference system) and then the consistency of the results is checked. Results show that the fuzzy controller can effectively minimize total delay and it is superior to compared methods.Öğe DESIGNING A FUZZY LOGIC CONTROLLER FOR A SINGLE INTERSECTION: A CASE STUDY IN GAZIANTEP(Yildiz Technical Univ, 2018) Dereli, Turkay; Cetinkaya, Cihan; Celik, NazmiyeThe traffic problem is a multidimensional problem and traffic lights are the key points to solving this problem. Thus, optimum green light duration helps reduce the traffic congestion. Queue length during red light and remaining vehicles in line after green light are important parameters for the determination of the green light duration. In this paper, a single intersection is taken into account with mentioned two inputs of fuzzy logic controller considering each intersection approach one by one. The output variables are chosen as phase selection and extend of green light duration. Unlike many other studies, these output variables are combined into one system based on modal distinction. Each approach is normalized according to the number of lanes to calculate a membership function value: because of different capacities of intersection approaches. The model is solved using MATLAB fuzzy inference system. The delay parameter is considered as performance measurement. Accordingly, proposed model is compared with various traditional models in literature. Finally, the model is evaluated using ANN (artificial neural network modeling) and ANFIS (adaptive neuro-fuzzy inference system) and then the consistency of the results is checked. Results show that the fuzzy controller can effectively minimize total delay and it is superior to compared methods.Öğe Emergency Shelter Site Selection in Maar Shurin Community of Idlib (Syria)(Penn State Univ Press, 2021) Cetinkaya, Cihan; Ozceylan, Eren; Isleyen, Selcuk KursatConstructing shelters is one of the most important precautions to reduce human casualties during disasters. The critical point in locating the shelter is the selection of the proper site. Unless shelters are placed in the right locations, they cannot serve as lifesaving tools. In this article, shelter site selection for a conflict area in Syria is taken into account. Notoriously, Syria has been facing these conflicts since 2011 and the intensity of the incidents is varying from region to region. Thus, in this study, Maar Shurin Community of Idlib is chosen as the study area because of its high airstrike/casualty statistics in 2018. A geographic information system (GIS) and its multicriteria decision analysis (MCDA) tools are used for determining the shelter site. Four different scenarios based on different criteria weights are generated to provide alternative solutions. It is believed that the outputs of the proposed methodology can help the local administration to solve its real-life problems.
