Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025Scopus SJRQ2/r/munzur/1927
  Forecasting daily natural gas consumption with regression, time series and machine learning based methods

  Yucesan, Melih; Pekel, Engin; Celik, Erkan; Gul, Muhammet; Serin, Faruk

  An effective short-term natural gas forecasting method contributes to social contributions and allows industrial chain elements to function effectively and minimize economic losses. We dealt with a comparative framework on the applicability of different methods in daily natural gas service (NGS) consumption forecasting. In this context, time series, machine learning, evolutionary and population-based approaches, and their hybrid versions are applied to the NGS data. Hybridized approaches are tested in the scope of NGS consumption forecasting for the first time in the literature in this study. The case of Turkey is handled, and its NGS data is used to demonstrate the comparative framework’s applicability. The comparative study is assessed in the lights of common forecasting accuracy measures of mean absolute percentage error (MAPE), R-squared (R2), and mean squared error (MSE). According to each method’s results, the seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and artificial neural network (ANN) hybrid model provides the most dominant performance with respect to MAPE. The lowest error was obtained with a MAPE value of 0.357 in this hybrid model constructed under seven neurons in its ANN structure. This model is followed by another hybrid model, autoregressive integrated moving average (ARIMA)-ANN, with a MAPE value of 0.5 under nine neurons in terms of accuracy performance. The worst performance value belongs to the Genetic algorithm-ANN hybrid model with a MAPE value of approximately 26%.

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  AtıfScopus16·WoS19·OpenAlex22

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/884
  Selection of the central composting facility for organic wastes through the Bayesian best worst method and Geographic information system incorporation: The case of Istanbul

  Güler, Mustafa; Samastı, Mesut; Yücesan, Melih; Gül, Muhammet; Çelik, Erkan; Ciner, Miraç Nur; Algorabi, Ömer

  The rapid increase in municipal solid waste (MSW) generation, driven by population growth and unplanned urbanization, has made the location of organic waste collection and disposal facilities a critical issue for metropolitan cities. Improper waste management practices threaten environmental sustainability, human health, and urban futures. This study applies multicriteria decision-making (MCDM) methods to identify the optimal placement for a central composting facility in Istanbul. A novel MCDM mechanism was developed, building on existing methods and addressing a gap in the literature. While determining the criteria, academic studies on this subject were researched in depth and the criteria used in these studies were brought together to obtain 28 criteria. The study assessed 28 criteria using the Bayesian Best-Worst Method (BWM) to determine their relative importance. These weights were then integrated into a Geographic Information System (GIS) to produce a suitability map, highlighting the most appropriate locations for the facility. Based on this analysis, the Çatalca Karacabey region was identified as the optimal site, ensuring reduced environmental impact and improved efficiency in organic waste management. The study contributes both theoretically and practically to the field, offering a robust framework for sustainable waste facility siting and providing actionable insights for urban governance. The study provides valuable insights for facility siting and urban governance, while highlighting areas for future research.

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  AtıfScopus5·WoS4

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• MakaleAçık Erişim2022Scopus SJRQ1/r/munzur/2844
  How Covid-19 pandemic and partial lockdown decisions affect air quality of a city? The case of Istanbul, Turkey

  Celik, Erkan; Gul, Muhammet

  The world is currently struggling with a new type of coronavirus (2019-nCoV) pandemic that first appeared in Wuhan, China, and then spread to almost all countries. As in other countries of the world, public authorities in Turkey are implementing many preventive and mitigating partial lockdown (PL) actions against the virus's effects. Some decisions and policies implemented before and after March 11, 2020, when the first virus case has been identified, have reduced people and traffic circulation, which has also turned into some improvements in air quality. At this point, this study aims to investigate how this pandemic affects the air quality of a metropolis. A case study of the city of Istanbul, the most affected city with more than half of Turkey's cases, is performed. In our analysis, we observe, compare, and discuss the impact of the COVID-19 pandemic and PL decisions on Istanbul city's air quality. We consider the particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NO), nitrogen oxides (NOx), and ozone (O3) concentrations. We used data from 19 air monitoring stations (AMSs) and obtained improvements in the air quality for the pandemic period. In summary, the concentration levels in PM10, NO2, NO, and NOx result in a clear decline in pandemic times compared to the normal times in Istanbul. On the other hand, a non-homogenous trend for SO2 and CO concentrations is observed for different AMSs. A partial increase in O2 concentration is obtained in the comparison of before and during the PL period.

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  AtıfScopus15·WoS13·OpenAlex22

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• Kitap BölümüYalnız künye2021/r/munzur/3439
  Fine–kinney-based occupational risk assessment using interval-valued pythagorean fuzzy vikor

  Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan

  This chapter aims at the adaptation of the Fine–Kinney occupational risk assessment concept into the VIKOR multi-attribute decision-making method with an interval-valued Pythagorean fuzzy set. The classical fuzzy set theory has been improved by proposing a number of extended versions. One of them is the Pythagorean fuzzy set. It has been firstly developed by Yager (IEEE Transactions on Fuzzy Systems 22:958–965, [1]). In this chapter, we use this type of fuzzy set with VIKOR since it reflects uncertainty in occupational risk assessment and decision-making better than other fuzzy extensions. To demonstrate the proposed approach applicability, a case study regarding the activities of the surface treatment area in a chrome plating unit of a gun factory is performed. Some additional analysis to test the solidity and validity of the approach is executed. Finally, the Python codes in the implementation of the proposed approach are given for scholars and practitioners for usage in further studies.

  AtıfScopus14·OpenAlex5

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• EditoryalYalnız künye2021/r/munzur/3324
  Preface

  Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan

  AtıfScopus2

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• Kitap BölümüYalnız künye2021/r/munzur/3246
  Fine–kinney-based occupational risk assessment using interval type-2 fuzzy vikor

  Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan

  In this chapter, we improved Fine–Kinney occupational risk assessment approach with interval type-2 fuzzy VIKOR (IT2FVIKOR). VIKOR is a compromise multi-attribute decision-making method proposed by Opricovic (1998). Similar to other compromised solution-based approaches, it considers the solution which is closest to the ideal. In this chapter, we adapted the interval type-2 fuzzy sets (IT2FSs) into VIKOR as it reflects the uncertainty well in decision-making. IT2FVIKOR algorithm under the Fine–Kinney concept provides a useful and solid approach to the occupational health and safety risk assessment. In addition to proposing this new approach, a case study is performed in a gun and rifle barrel external surface oxidation and coloring unit of a gun factory. A validation and a sensitivity analysis is also attached to this study. Finally, the proposed approach is implemented in Python.

  AtıfScopus4·OpenAlex5

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• MakaleAçık Erişim2021Scopus SJRQ2/r/munzur/3230
  Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

  Pekel, Engin; Gul, Muhammet; Celik, Erkan; Yousefi, Samuel

  The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

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  AtıfScopus7·WoS5·OpenAlex7

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• Kitap BölümüYalnız künye2021/r/munzur/2963
  Fine–kinney-based occupational risk assessment using fuzzy best and worst method (f-bwm) and fuzzy mairca

  Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan

  The best-worst method (BWM) proposed by Rezaei (Omega 53:49–57, [1]) is an MCDM method used to achieve the weights of the criteria by making fewer pairwise comparisons and using more consistent decision matrices. In this chapter, instead of crisp numbers, triangular fuzzy numbers that reflect the uncertainty well in real-world problems are used in integration with the BWM method (Guo and Zhao in Knowl Based Syst 121:23–31, [2]) in determining factor weights. Using the fuzzy best and worst method (F-BWM), a model based on Fine–Kinney occupational health and safety risk assessment method was developed for the first time in the literature. Three parameters of Fine–Kinney method are weighted by the mathematical models of F-BWM. Then, the risks are prioritized by fuzzy multi-attribute ideal real comparative analysis (F-MAIRCA). A case study was conducted to demonstrate the feasibility of the approach, and besides this case study, a comparative study was also conducted to test the validity of the proposed approach. This approach led to the conclusion that Fine–Kinney’s method, BWM, MAIRCA, and triangular fuzzy sets make the risk decision-making process more dynamic, taking into account the benefits of these methods individually or in integration.

  AtıfScopus11·OpenAlex12

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• Kitap BölümüYalnız künye2021/r/munzur/2873
  Fine–kinney-based occupational risk assessment using hexagonal fuzzy multimoora

  Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan

  Hexagonal fuzzy numbers (HFNs) can be used as a proficient logic to simplify understanding of ambiguity information. HFNs present the usual information in a comprehensive way and also the ambiguity section can be exemplified in a reasonable way. In this chapter, we proposed an improved Fine–Kinney occupational risk assessment approach using a well-known MCDM method Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) using hexagonal fuzzy numbers. Since the mere MULTIMOORA has failed to handle uncertainty and vague information which usually exist in real world problems, we follow integration of HFNs and MULTIMOORA (HFMULTIMOORA). To show the applicability of the novel approach, a case study of risk assessment of a raw mill in cement plant was provided. Comparative analysis with using two aggregation tools as reciprocal rank method and dominance theory are carried out. Finally, the Python implementation of the proposed approach is implemented to be effective for those concerned in the future.

  AtıfScopus7·OpenAlex5

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• Kitap BölümüYalnız künye2021/r/munzur/2656
  Fine–kinney-based occupational risk assessment using intuitionistic fuzzy todim

  Gul, Muhammet; Mete, Suleyman; Serin, Faruk; Celik, Erkan

  This chapter applies a novel occupational risk assessment approach which merges TODIM with the Fine–Kinney method under the intuitionistic fuzzy set concept. Risk parameters of Fine–Kinney and OHS experts are weighted by an intuitionistic fuzzy weighted averaging (IFWA) aggregation operator. Hence, hazards are quantitatively evaluated and prioritized using the proposed approach. To illustrate the novel risk assessment approach, processes of the gun and rifle assembly line of a factory are handled. A comprehensive risk assessment is carried out to improve operational safety and reliability in the industry. We adapt intuitionistic fuzzy sets in the existing study since they reflect uncertainty with the aid of their membership and nonmembership functions in decision-making better than classical fuzzy extensions. An additional sensitivity analysis by changing the attenuation parameter of TODIM is performed to test the validity of the approach. Finally, the Python codes in the implementation of the proposed approach are given for scholars and practitioners for usage in further studies.

  AtıfScopus5·OpenAlex9

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