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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• 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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• 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

  İncele
• Kitap BölümüYalnız künye2021/r/munzur/2642
  Fine–kinney-based occupational risk assessment using single-valued neutrosophic topsis

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

  Neutrosophic sets are initially recommended by Smarandache (First international conference on neutrosophy, neutrosophic logic, set, probability, and statistics. University of New Mexico, Gallup, NM, pp 338–353, 2002 [1]). These sets reflect uncertainty and vagueness in real-world problems better than classical fuzzy set theory. It takes into consideration three decision-making situations called indeterminacy, truthiness, and falsity. In Zadeh traditional fuzzy set theory, there is just membership function fuzzy set degree. But, in neutrosophic environment, it considers three membership functions. Unlike intuitionistic fuzzy sets, an indeterminacy degree is considered. In this chapter, we applied a special form of neutrosophic set as single-valued neutrosophic set (SVNs) with the technique for order preference by similarity to ideal solution (TOPSIS) under the concept of Fine–Kinney occupational risk assessment. Since the mere TOPSIS has failed to handle imprecise and vague information which usually exist in real-world problems, we follow the integration of SVNs and TOPSIS. To demonstrate the applicability of the novel approach, a case study of risk assessment of a wind turbine in times of operation was provided. Comparative analysis with some similar approaches and sensitivity analysis by changing the weights of Fine–Kinney parameters are carried out. Finally, the Python implementation of the proposed approach is executed to be useful for those concerned in the future.

  AtıfScopus12·OpenAlex9

  İncele
• Kitap BölümüYalnız künye2021/r/munzur/2423
  Fine–kinney occupational risk assessment method and its extensions by fuzzy sets: A state-of-the-art review

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

  The Fine–Kinney method (Fine in J Saf Res 3:157–166, 1971; Kinney and Wiruth in Practical risk analysis for safety management. Naval Weapons Center, pp 1–20, 1976), which was first introduced as an occupational health and safety risk analysis tool in the 1970s, is a systematic methodology that provides a mathematical formula for calculating the risk that arises due to a specified hazard. In the traditional version of Fine–Kinney as suggested in its original version, a risk score (RS) is calculated as a result of mathematical multiplication of probability (P), exposure (E), and consequence (C) parameters. These calculated risk scores are used to establish priorities for the corrective efforts in order to eliminate risks or reduce their effects to a reasonable level. This simple and useful method is preferred and implemented by small and medium-sized enterprises. In the academic literature, it has been applied for many risk analysis problems, although it includes several drawbacks recently revealed. In this method, no weight assignment is made for each risk parameter. Also, it is hard to assess consequence, exposure, and probability, precisely. Multi-criteria decision making (MCDM) is a pool of methods used in occupational health and safety risk analysis both by international standard-setting organizations and scholars from the literature. In classical MCDM methods, performance values and weights of decision criteria are known precisely and are specified with crisp numbers. However, many real-world problems contain uncertainties, and the knowledge and judgment of experts cannot be expressed precisely. Fuzzy-based MCDM methods, which are developed to reflect types and degrees of uncertainties better, produce more accurate results compared to classical methods. In this chapter, we first present the basics of Fine–Kinney method, including its implementing procedure, basic terminology, and drawbacks. Then, we provide a state-of-the-art review of Fine–Kinney occupational risk assessment method and its extensions by fuzzy sets. Graphical results obtained from the review are demonstrated to show the current state. Future work suggestions are also included to the chapter to show the possible gaps and possible opportunities.

  AtıfScopus12·OpenAlex12

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• Kitap BölümüYalnız künye2021/r/munzur/2381
  Fine–kinney-based occupational risk assessment using interval type-2 fuzzy qualiflex

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

  In this chapter, we improved Fine–Kinney occupational risk assessment approach with interval type-2 fuzzy QUALIFLEX (IT2FQUALIFLEX). QUALIFLEX is an outranking multi-attribute decision-making method proposed by an extension of the Paelinck’s (Pap Reg Sci Assoc 36:59–74,[1]), generalized Jacquet-Lagreze’s permutation method. Similar to other outranking solution-based approaches, it considers the solution which is a comparison of hazards. In this chapter, we adapted the interval type-2 fuzzy sets (IT2FSs) into QUALIFLEX as it reflects the uncertainty well in decision-making. IT2FQUALIFLEX 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 the chrome plating unit. A validation is also performed in this study. Finally, the proposed approach is implemented in Python.

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  AtıfScopus7·OpenAlex7

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