Yazarlar
- Akkurt, Nevzat(10)
- Gul, Muhammet(7)
- Işık, Erdem(7)
- Yucesan, Melih(6)
- Acar, Ramazan(5)
- Konurhan, Zekeriya(5)
- Bayrakdar, Cihan(4)
- Kahraman, Gökhan(4)
- Keserci, Ferhat(4)
- Tuğan, Volkan(4)
- Utku, Anıl(4)
- Çılğın, Zeynel(4)
- İnallı, Mustafa(4)
- Göçmen, Elifcan(3)
- Korkmaz, Meral(3)
- Utku, Anil(3)
- Akbas, Ergun(2)
- Aksağan, Zülfükar(2)
- Akyol, Sinem(2)
- Bjarnadottir, Brynhildur(2)
- Black, Andrew(2)
- Celik, Erkan(2)
- Demir, Yakup(2)
- Derse, Onur(2)
- Erdem, Necla Seval(2)
- Evans, Ian S.(2)
- Geçkil, Tacettin(2)
- Gunnarsdottir, Gunnhildur E.(2)
- Güçlü, Yavuz Selim(2)
- Hudaverdi, Burcu(2)
- Imak, Andaç(2)
- Kjartansson, Bjarki T.(2)
- Koycegiz, Cihangir(2)
- Oddsdottir, Edda S.(2)
- Oskarsson, Hlynur(2)
- Saplıoğlu, Kemal(2)
- Sarı, Sacit(2)
- Shao, Shuangquan(2)
- Sigurdsson, Bjarni D.(2)
- Susam, Selim Orhun(2)
Yayınlar
- MakaleAçık Erişim2025Elazığ İli Örneğine Ait On-Grid Çatı Üstü Güneş Enerji Santralinin Yıllık Gerçek Üretim Verilerinin PV*SOL Simülasyon Değerleri ile Karşılaştırılması
Celik, Ertugrul; Özdemir, Osman
Geçmişten bugüne kadar insanoğlunun temel gereksinimlerinin karşılanması için enerjiye ihtiyaç duyulmaktadır. Dünyada nüfusun hızlı bir şekilde yükselmesi, teknolojik faaliyetlerin gelişimiyle birlikte enerji tüketimi gün geçtikçe artmaktadır. Günümüzde artan enerji talebini karşılamak için fosil yakıtlar ve yenilenebilir enerji kaynakları kullanılmaktadır. Yenilenebilir enerji kaynakları, doğal süreçler aracılığıyla sürekli olarak yenilenen ve fosil yakıtlarla karşılaştırıldığında düşük emisyon değerlerine sahip olan çevre dostu enerji üretim kaynaklarıdır. Güneş enerjisi sistemlerindeki teknolojik ilerlemeler, maliyetlerin düşmesi ve verimliliğin artması, güneş enerjisini yenilenebilir enerji kaynakları arasında güçlü bir alternatif haline getirmektedir. Özellikle arazi ve çatılara kurulan Güneş Enerji Santrallerinin (GES) sayısı gün geçtikçe artmaktadır. Bu çalışmada Elazığ ili sınırları içinde sanayi bölgesinde bir işletmenin çatısına kurulmuş şebekeye bağlı çatı (on-grid) üstü GES sistemi ele alınmıştır. Çatı üstü GES toplam 165 adet, 545 Watt gücünde monokristal panellerden oluşmakta olup toplam gücü 89,9 kWp’tir. Sistem performansını artırmak amacıyla seçilen alan için en iyi yönelimi ve eğim açısını bulmak amacıyla PV*SOL yazılımı kullanıldı. Bu çalışmada mevcut GES sisteminin bir yıllık enerji üretim verileri aylara göre incelenerek, PV*SOL simülasyonunda elde edilen benzetim sonuçları ile karşılaştırılarak PV*SOL yazılımının doğruluk oranı değerlendirilmiştir. Çalışma sonucunda PV*SOL simülasyon programından elde edilen veriler ile çatı üstü GES gerçek verileri arasında %0,03 fark olduğu tespit edilmiştir. Bu sonuçlar göz önüne alındığında, uygulaması yapılan enerji sisteminin teknik ve ekonomik olarak uygun olduğu söylenebilir.
AtıfOpenAlex1
İncele - MakaleAçık Erişim2025Predicting daily energy consumption in ev charging stations using tree-based models
Ertarğın, Merve
Electric vehicles (EVs) have emerged as a cornerstone in the transition toward sustainable transportation, offering a cleaner alternative to conventional fossil fuel-based mobility. Their integration into modern transportation systems not only supports global decarbonization efforts but also requires robust energy management solutions to address the growing demands on charging infrastructure. Ensuring efficient energy consumption forecasting is vital for optimizing EV charging station operations and maintaining grid stability. This study aims to predict the daily energy consumption of an EV charging station using a public dataset. Key features such as charging duration, vehicle count, mean and median connection time were utilized for the prediction. Both the original dataset and its z-score-cleaned version were employed to evaluate the models' performance under different data conditions. Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) models were implemented, with hyperparameter optimization conducted via GridSearchCV. The results revealed that all four models achieved high accuracy in predicting daily energy consumption across both datasets, with R2 values exceeding 0.97. Specifically, for the z-score-cleaned dataset, LightGBM demonstrated the best performance with the lowest RMSE (37.35 kWh) and MAE (28.93 kWh), as well as the highest R2 value (0.9727) followed by XGBoost, RF and DT.
AtıfOpenAlex1İncele - MakaleAçık Erişim2025Application of GIS in Flood Risk Analysis and Mitigation Strategies: The Case of Uluova and İkitepe Streams
Korkmaz, Meral; Uymaz, Muhammed
In recent years, the frequency and severity of floods have increased significantly due to the effects of global climate change, leading to serious economic and social losses, particularly in agricultural production areas. Floods not only damage cultivated lands but also negatively affect the livelihoods and socio-economic structures of local communities. This situation necessitates accurate identification of flood risk in agricultural areas and timely implementation of appropriate mitigation measures. In this study, flood risk was investigated in agricultural lands surrounding the Uluova Stream located between Doğankuş, Karşıbağ, and the Keban Dam and the İkitepe Stream, which flows through the center of Mollakendi in Elazığ Province, Türkiye. These areas are characterized by intensive agricultural activities. Using Geographic Information Systems (GIS), seven key parameters distance to streams, land use, aspect, slope, soil structure, precipitation, and geological features were analyzed, and corresponding thematic maps were generated. Flood risk levels were determined through spatial analysis and classification of these parameters using ArcGIS software. Based on the results, areas with high flood risk were identified, and both structural and non-structural mitigation measures were proposed for these zones. The findings offer valuable insights to support sustainable agricultural production and rural development by minimizing potential flood damages and reducing associated economic risks in the region.
- MakaleYalnız künye2025Scopus SJRQ2Impact of green innovation on environmental issues: Findings from BRICS-T countries based on spatial analysis
Han, Ayşegül; Pehlivan, Ceren; Konat, Gökhan; Koncak, Ahmet
This study examines the impact of green innovations on environmental sustainability in BRICS-T countries between 2008 and 2021. Using indicators such as renewable energy consumption, R&D investments, industrial design activities, and carbon emissions, the analysis employs both conventional and spatial panel estimation techniques. The findings reveal that investments in renewable energy play a crucial role in reducing carbon emissions and positively contribute to environmental sustainability. However, the effectiveness of environmental innovation policies and R&D expenditures in mitigating emissions remains limited, particularly in the short term. Additionally, industrial design activities are identified as a significant contributor to rising emissions, highlighting the need for a transition toward more sustainable industrial practices. The study also uncovers spatial dependencies among countries, demonstrating that carbon emissions in one nation can influence those in neighboring countries, emphasizing the necessity of regional cooperation and coordinated environmental policies. Based on these findings, the study provides policy recommendations to enhance the effectiveness of green innovations, stressing the importance of increasing investments in renewable energy, aligning R&D expenditures with environmentally sustainable technologies, and promoting sustainable industrial practices across BRICS-T countries.
AtıfScopus2WoS2OpenAlex3
İncele - Kitap BölümüYalnız künye2025Bayesian Best–Worst Method Application for Assessing the Potential Effecting Areas of Climate Change: A Case Study in Turkey
Konurhan, Zekeriya; Yücesan, Melih; Gul, Muhammet
‘Climate Change’ is a term used to refer to global changes in the Earth's average temperature and the consequences of changes in temperature. More clearly, it demonstrates changes in temperature and weather patterns over time. The emergence of this situation directly or indirectly affects irreparable damages and major/minor disasters in many areas. Determining the answer to the question of which of these areas has an impact at what level of importance will provide guidance on which one should be prioritized in carrying out an effective treatment. Therefore, in this study, this problem is ad-dressed as a multi-criteria decision problem using Bayesian Best–Worst Method. The potential impacts have been categorized under 8 main areas and weighted via participating of 8 competent experts (geography, emergency disaster management, geomorphologist, climate scientist etc.) in the field of climate change. The study results determined that B (Ecosystems and Water Resources) has the most critical impact area, with 0.200. The least essential domain is G4 (Adaptation and Resilience Strategies in the Tourism Industry), which weighs 0.012. The results showed that they provide important clues regarding which impacts should be addressed first.
- MakaleAçık Erişim2025Scopus SJRQ2Forecasting 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%.
AtıfScopus16WoS19OpenAlex22İncele - MakaleAçık Erişim2025Scopus SJRQ1Examining the levels of awareness, anxiety, and hope regarding global climate change among university students participating in activities at youth offices
Yildiz, Metin
Background: Climate change is one of the most significant issues of today. This study aims to investigate climate change awareness, anxiety, and hope levels among university students who participate in activities at youth offices and examine the relationship between them. Methods: The study population consisted of student members who were actively engaged in Youth Offices located within four public universities in the Eastern Anatolia Region of Türkiye. The study sample includes 881 volunteer participants, consisting of 345 (39.2%) male and 536 (60.8%) female students. The data were analyzed employing IBM SPSS 25. Statistical analyses included the independent samples t-test, one-way ANOVA, Tukey’s post-hoc test, and Pearson’s correlation coefficient. Results: The results indicate that university students have moderate levels of climate change awareness, anxiety, and hope. A positive correlation was observed, wherein higher awareness levels were associated with increased anxiety and hope. Male students demonstrated higher awareness and anxiety levels than female students. Among participants, students from 1. University exhibited the highest levels of climate change awareness, anxiety, and hope. Additionally, graduate students and individuals living in rural areas showed higher awareness and anxiety levels, whereas individuals with better economic conditions exhibited lower anxiety and hope levels. Conclusion: Given these results, it is recommended that universities develop and implement educational programs to enhance climate change awareness and extend these initiatives across campuses.
- MakaleYayıncıda açık erişim2025Scopus SJRQ1Experimental analysis of a double-slope solar still integrated with parabolic trough solar collector using nanofluid
Tuğan, Volkan; İnallı, Mustafa; Işık, Erdem
The use of solar energy in the treatment of saline water offers a sustainable and low-cost solution to the increasing global fresh water demand. In this direction, solar still systems separate water vapor from saline water by directly using solar radiation and then condense it to obtain pure water. However, their fresh water productivity is relatively low. Therefore, in this study, the fresh water productivity and thermal efficiency of the double slope solar distillation unit integrated with parabolic trough solar collector, which operates completely on solar energy, were investigated experimentally. In addition, the effect of water-based nanofluid containing multi-walled carbon nanotubes on the performance of the solar still was investigated. All the stills produced were compared in terms of fresh water productivity and thermal efficiency. According to the results obtained from the study, the highest daily productivity was found to be 1963.8 g/m2/day, 2008.5 g/m2/day, 2592.0 g/m2/day and 2937.6 g/m2/day for SSSS, DSSS, PTSC integrated DSSS and both NF and PTSC integrated DSSS, respectively. It was observed that the daily productivity of DSSS, DSSS with integrated PTSC, and DSSS with both PTSC and NF were 6.6 %, 42.2 %, and 49.6 % higher than SSSS, respectively. Additionally, it was observed that the thermal efficiency of DSSS using PTSC and NF was lower compared to SSSS due to its larger system area.
AtıfScopus13WoS8OpenAlex10İncele - MakaleYalnız künye2025Scopus SJRQ1Median-Linear model for solar radiation estimation by comparison with ANFIS and Angström-Prescott methods
Acar, Ramazan; Saplıoğlu, Kemal; Güçlü, Yavuz Selim
This paper aims to propose a novel approach to estimating solar radiation in relation to sunshine duration. A hybrid Median-Linear (MedLin) model is presented including both the median value and the linear solar radiation equation. First stage is concerned with two-stage alternative of the proposed model with inputs and outputs exhibiting a seasonality structure and it is seasonally adjusted using median values. In the second stage, the linear equation for the difference values is determined with estimations the difference in solar radiation. Finally, the median value and the estimated difference value are added. Monthly total solar radiation values are estimated using monthly total sunshine duration data from Mardin, Adana, Bozova, Hakkari, Şırnak, and Van stations in Southeast of Türkiye for the application. The predictions of the MedLin approach are compared with the results of the Angström-Prescott, ANFIS, and Mean-Linear (MeanLin) models. As for the prediction of monthly total solar radiation data at Mardin and Hakkari stations, the proposed MedLin approach yields the best results compared to other methods with mean absolute percentage error 3.508% and 8.951%, respectively. Angström-Prescott model for Adana, MeanLin method for Bozova, ANFIS model for Şırnak, and MeanLin model for Van provided the best predictions with mean absolute percentage error 3.721%, 3.285%, 4.278%, and 13.101%, respectively. The results show that the MedLin technique proposed in this paper is useful for solar radiation estimation.
AtıfWoS1İncele - MakaleAçık Erişim2025Scopus SJRQ1Hybrid GA-ConvLSTM for data-driven prediction of climate variables: a case study of the most biodiverse cities in India
Utku, Anıl; Akyol, Sinem
Climate variables affect ecosystems’ structure, sustainability, and biodiversity. Monitoring and analysis of climate variables are essential for the management of ecosystems and the conservation of biodiversity. Temperature affects the distributions, habitats, and phonological events of living things in ecosystems. Humidity is essential for meeting plants’ water needs and the habitat’s quality and health. Dew point is essential for water quality, balance, plant health, and microclimate. Wind speed is essential for pollinating plants, microhabitats, and the health of living things. For such reasons, accurate prediction of climate variables using artificial intelligence methods can effectively predict the future of ecosystems and biodiversity and develop future strategic plans. This study aimed to predict the climate variables of temperature, humidity, dew point, and wind speed of Bhubaneshwar, Dehradun, Guwahati, Mysore, and Nagpur, among the cities with the highest biodiversity in India. It aimed to determine the long-term impacts of climate change on managing ecosystems and biodiversity protection. The hyperparameters of the ConvLSTM model, developed using CNN and LSTM, were optimized with a genetic algorithm, and the GA-ConvLSTM hybrid model was created. GA-ConvLSTM was tested with XGBoost, SVM, ANFIS, CNN, LSTM, and base ConvLSTM using approximately 15 years of hourly, up-to-date, and real-time climate variable data of cities. This work is one of the pioneering studies using GA to optimize deep learning hybrid models for multi-city climate prediction through extensive long-term high-resolution datasets. The experimental results reveal that GA-ConvLSTM outperforms conventional models, producing an R2 score exceeding 0.9 for each city across all climate variables. GA-ConvLSTM produced temperature predictions with 0.833 RMSE and 0.995 R2 for Bhubaneswar, humidity predictions with 3.461 RMSE and 0.990 R2, dew point predictions with 0.632 RMSE and 0.995 R2, and wind speed predictions with 2.278 RMSE and 0.993 R2. The experimental findings confirm the improved performance over conventional models due to SVM and XGBoost failing to achieve these R2 values and high RMSE scores. The research meets three principal obstacles because of the combination of stochastic climate datasets and complex nonlinear patterns, which also encounter limitations in conventional methods for managing scale-dependency relationships and manual parameter adjustments. This approach delivers three main strengths because it combines CNN and LSTM networks through genetic programming while tracking temporal and spatial relationships and processing noisy data automatically, leading to improved results compared to other methods. Unlike other published models in the field, the hybrid model GA-ConvLSTM combines optimized CNN and LSTM elements with genetic algorithm elements. The proposed model received its first application in this research for multi-city climate prediction through training with 15 years of hourly data. Standard methods encounter challenges when detecting long-term dependencies, but GA-ConvLSTM successfully addresses this issue.
AtıfScopus1OpenAlex1İncele - MakaleYalnız künye2025Scopus SJRQ1Rainfall trends in the transboundary Euphrates-Tigris River Basin
Acar, Ramazan; Akbas, Ergun; Koycegiz, Cihangir; Esit, Musa
Climate change and anthropogenic factors have a significant impact on rainfall variability. In this study, the trends of monthly total precipitation in the transboundary Euphrates-Tigris River Basin (ETRB), where these effects are intensely observed, were analyzed by trend analysis methods with various perspectives. After decomposing the monthly total rainfall series (1964–2021) obtained from 18 stations representing 12 months of the year, statistical (Mann-Kendall, Wilcoxon, Onyutha), graphical (Innovative Polygon Trend Analysis, IPTA), Trend Polygon Star Concept, TPSC), sub-trend (Onyutha) and spatial trend analyses were performed. Based on the results, it was observed that rainfall in the basin has significant spatial and temporal variability. A monotonic strong decreasing trend was observed in cold months with high rainfall, while an increasing trend was observed in warm months with low rainfall. Extreme rainfall during the winter months has shifted towards the end of the cold months. Graphical and statistical analysis methods have shown strong consistency in identifying monotonic trends. However, the implementation of various methods provides specific insight (transition, sub-trend, etc.) into the temporal and spatial variability of rainfall. Sub-trend analysis shows that rainfall characteristics in the cold months show a significant change before and after the 1990–2000 period. In warmer months, this variability is significantly affected by local and global driving forces. The central and marginal parts of the ETRB have different rainfall characteristics. During cold months, the monotonic trend (decreasing) is significant in the central part. In warm months, the monotonic trend (increasing) increases its intensity in the eastern parts.
AtıfScopus12WoS10OpenAlex14İncele - MakaleYalnız künye2025Scopus SJRQ2Exploring the Grazing Land Footprint’s Stochastic Behavior in the Top Dairy and Meat-Producing Nations
Adali, Zafer; Yeter, Fatih; Çoban, Mustafa Necati; Sarı, Sacit
The Sustainable Development Goals (SDGs) are a set of influential and easily understood sustainability principles. SDG2 defines Zero Hunger, which is once again a key goal in the SDGs. A sustainable food system depends on the grazing land footprint, one of the elements of the ecological footprint. For food security to be maintained, the grazing land footprint has to be taken into consideration. In order to provide adequate global food systems and mitigate climate change, research on the stochastic features of grazing land footprint is crucial. The goal of this research is to look at the stochastic characteristics of the grazing land footprint in 13 nations that produce almost two-thirds of the world’s milk and meat. The study includes data for 13 nations over the years 1961–2022. Several unit root tests are conducted to provide more precise data about the effectiveness of the policy shocks, including whether or not they allow for repeated, smooth structural breakdowns. The results show that because the series is prone to following its long-term trends, shocks cannot lead it to diverge from those trends. Thus, any shocks brought on by policies cannot have a lasting effect on the series. However, the series includes a unit root. The unit root tests have made it clear that any policy shocks would have a long-lasting effect on the series and that policymakers may set a goal for the series. Policy suggestions have been provided for the investigated nations in conjunction with these results.
AtıfScopus2WoS2OpenAlex8İncele - MakaleAçık Erişim2025Scopus SJRQ2Multiscale Time Series Modeling in Energy Demand Prediction: A CWT-Aided Hybrid Model
Sezer, Elif; Yıldırım, Güngör; Özdemir, Mahmut Temel
In the contemporary energy landscape, the increasing demand for electricity and the inherent uncertainties associated with the integration of renewable resources have rendered the accurate and reliable forecasting of short- and long-term demand imperative. Energy demand forecasting, fundamentally a time series problem, can be inherently complex, nonlinear, and multi-scale. Therefore, interest in artificial intelligence–based methods that provide high performance for short- and long-term forecasting, rather than traditional methods, has increased in order to solve these problems. In this study, a hybrid artificial intelligence model based on LSTM, GRU, and Random Forest, utilizing a distinct mechanism to address these types of problems, is proposed. The Multi-Scale Sliding Window (MSSW) approach was utilized for the model’s input data to capture the dynamics of the time series at different scales. The optimization of windows was conducted using the Continuous Wavelet Transform (CWT) method to determine the optimal window sizes within the MSSW structure in a data-driven manner. Experimental studies on Panama’s real energy demand data from 2015 to 2020 show that the CWT-aided MSSW-hybrid model forecasts better with lower error rates (0.007 MAE, 0.009 RMSE, 1.051% MAPE) than single models and manually determined window sizes. The results of the study demonstrate the importance of hybrid structures and window optimization in energy demand forecasting.
AtıfScopus1OpenAlex2İncele - MakaleYalnız künye2025Scopus SJRQ2The impact of global value chains on climate change
Ateş, Erdem; Şanlısoy, Selim
Climate change can be defined as the increase in average temperatures due to rising greenhouse gas (GHG) emissions, predominantly due to human activities. Carbon emissions have increased at an alarming rate with the emergence of globalization, with its accompanying global value chain, and the consequent increase in global production volume with its polluting sectors. To combat this alarming situation, production with environmentally friendly technologies has emerged recently, and developing countries are embracing more and more costly environmentally friendly production techniques, although they are disadvantaged when it comes to covering the costs. However, it has been observed that they stay behind in this race to adopt environmentally friendly techniques compared to developed countries. The aim of the study, which is a novel contribution to the existing literature, is to examine the relationship between the global value chain and climate change by considering the distinction between developed and developing countries. The study used the data of 156 countries from the years 2000–2018. The results obtained from the generalized method of moments (GMM) show that participation in global value chains has different effects in developed and developing countries regarding carbon emissions. When the level of participation in GVC (global value chain) and FVA (foreign value added) increases, countries’ carbon emissions decrease, and it increases when the DVA (domestic value added) level increases. As a result, in particular, developing countries should green their production methods, and for this purpose, financial support mechanisms should be established.
AtıfScopus8WoS4OpenAlex7İncele - MakaleAçık Erişim2025Scopus SJRQ1Evaluation of Green Deal compliance performance with a hybrid comparative multi-attribute decision model
Karadag, Mehmet; Gul, Muhammet; Yucesan, Melih; Ortiz-Barrios, Miguel; Ishizaka, Alessio; Khan, Jehangir
The environmental and economic transformation initiated by the European Green Deal is influencing not only EU member states, but also other countries engaged in trade with the EU. This transformation mandates a shift towards sustainable practices in sectors such as agriculture, industry, transportation, energy, and finance. Compliance with the Green Deal is increasingly crucial for countries, particularly those with significant commercial ties to the EU. However, differing levels of development lead to varying degrees of implementation. While studies on the Green Deal compliance of EU member states exist, an evaluation of Türkiye—a candidate EU member with substantial trade relations—would address a significant gap in the literature. This study introduces a novel hybrid comparative multi-attribute decision model that combines subjective (Best Worst Method and its derivatives) and objective (Entropy) weighting to determine the relative importance of 14 sub-criteria under three main criteria: “Reducing” the impacts of climate change, “Protecting” the planet and health, and “Enabling” a green and just transition. Using the Ratio Product Model (RPM), Türkiye's Green Deal compliance from 2017 to 2021 was evaluated. Sensitivity analysis was conducted to validate the robustness of the findings, and comparisons were made using various multi-attribute decision models, including SAW, TOPSIS, and VIKOR, as well as benchmarking against EU countries. The criteria weights, derived from a combination of subjective and objective methods, highlighted the dominant importance of the “Reducing” criterion across all methods. The “Enabling” and “Protecting” criteria showed slight variations in importance depending on the method. The RPM results indicated that 2021 was Türkiye's best-performing year in terms of Green Deal compliance, while 2017 showed the weakest performance. Despite some fluctuations between 2018 and 2019, Türkiye demonstrated significant progress overall. The study not only advances the methodology for evaluating Green Deal compliance but also provides actionable insights for policymakers and industry stakeholders, enabling more informed decision-making. By highlighting Türkiye's trajectory, the findings offer strategic guidance for non-EU countries aiming to align with Green Deal objectives, fostering sustainable development and strengthening economic ties with the EU.
AtıfScopus11OpenAlex11İncele - MakaleYalnız künye2025Scopus SJRQ1Seasonally adjusted periodic time series for Mann-Kendall trend test
Güçlü, Yavuz Selim; Acar, Ramazan; Saplıoğlu, Kemal
In this study, temperature data trend analysis is conducted, which is thought to be influenced by global warming, climate change, and local factor impacts. The main objective of this paper is to obtain an acceptable autoregressive correlation value for the MK test. For this purpose, seasonality (periodicity) especially in monthly time series is adjusted. Autoregressive correlation and homogeneity test values decrease after seasonal adjusting, but reasonable results are not achieved. Then, prewhitening procedure is also applied to the seasonally adjusted data. This process resulted in the data becoming both homogeneous and free autoregressive correlation. The final version of the time series is suitable for the MK test. Furthermore, the time series are divided into different time intervals, and the efficacy of the method is investigated. The results demonstrated that the method is suitable for time series with less than 30 years of data. The study demonstrated that the proposed method enhances the reliability of the data. Also, multiplication by 12 (months) allows the MK test with Z score in place of the Student's t-test in short-term data sets. This suggested methodology can be used to identify MK trend conditions in monthly time series. The application is based on monthly and annual average temperature data between 1957 and 2022 from three stations within the Kızılırmak basin (Çankırı, Kırşehir, Sivas stations) and one station within Seyhan Basin (Adana station) in Türkiye. The test results exhibited a significant increasing trend.
AtıfScopus37WoS34OpenAlex41İncele - MakaleAçık Erişim2025Scopus SJRQ3Experimental and Theoretical Study on the Freshwater Productivity of a Single-Slope Solar Still
Tuğan, Volkan; Işık, Erdem; İnallı, Mustafa
Converting saline water into freshwater using renewable energy resources is crucial to address global shortages of freshwater. This study aims to investigate the freshwater productivity of a single-slope solar still (SSS) operating with solar energy, one of the renewable energy sources. The main motivation of this study is to evaluate the experimental, theoretical, and economic performance of the solar still under climate conditions that have not been sufficiently studied before. Experimental analyses were conducted under the climatic conditions of Tunceli, a city located in the Eastern Anatolia Region of Türkiye. In addition, a comprehensive theoretical analysis was carried out using energy equations, and the experimental and theoretical results were compared with each other. Finally, the economic feasibility of the system was evaluated through a cost analysis. The SSS's experimental and theoretical productivity were found to be 1883.9 and 2458.7 g/m2/day, respectively. The experimental thermal efficiency for this still was 15.1%, whereas the theoretical thermal efficiency was 19.7%. It was concluded that the theoretical productivity and efficiency values were close to the experimental values and the difference between them originated from the assumptions of theoretical calculations. The cost of water distilled by the single-slope solar distillation unit was found to be $0.077/kg, and the payback period was found to be 9 months.
AtıfScopus2WoS3OpenAlex2İncele - MakaleAçık Erişim2025Scopus SJRQ1Investigating forest fire causes through an integrated Bayesian network and geographic information system approach
Konurhan, Zekeriya; Yucesan, Melih; Gul, Muhammet
Forests are among the most critical natural resources worldwide and are essential for a sustainable ecosystem. In recent years, climate change and human activities have increased their impact on forests, and forest fires-especially in summer- have caused significant forest losses globally. This paper integrates Bayesian Network (BN) and Geographic Information Systems (GIS) to pinpoint the possible causes of forest fires and analyze their complex interactions. The study used data from 1465 fires between 2017 and 2022 in Muğla province in southwestern Türkiye. Within the scope of the study, 11 variables were chosen, such as elevation, slope, aspect, wind speed, population density, and road network, to build a BN model that combines physical and human geographical features. These variables overlapped using the pairwise intersect tool from ArcGIS Pro during the BN model setup, and probability values were calculated. The overall probability of fire in the BN model was determined to be 0.81, with probabilities ranging from 0.81 to 0.56 in low-altitude, moderately sloped, and south-facing areas. Scenario analyses examined fire risk under different conditions, highlighting the most influential variables for fire prevention efforts. The study identified fire-prone areas as spatial data, revealing that densely forested coastal and certain inland regions are at higher risk, whereas bare high-altitude areas with steep slopes pose lower risk. The BN model can be further enhanced by incorporating additional variables, making it a valuable tool for future fire risk assessment and mitigation strategies research.
AtıfScopus5WoS5OpenAlex7İncele - MakaleAçık Erişim2024İklim Krizinin Medyada Ele Alınışı: Akademik Eğilimlerin ve Çalışmaların Bibliyografik Analizi
Kenes, Hatice
Bu çalışma, 2000-2023 yılları arasındaki 23 yıllık süreçte Türkiye’de iklim değişikliğine dair medyada yer alan haberleri ve söylemleri ele alan akademik çalışmaları analiz etmektedir. İklim krizi, günümüzde en büyük çevresel tehditlerden biri olarak kabul edilmekte olup, Türkiye de bu etkileri derinden hissetmektedir. Bu bağlamda medya, farkındalık yaratma ve bilgilendirme konusunda kritik bir rol oynamaktadır. Özellikle de medyanın kitleselliği ve kitlelere ulaşma hızı göz önüne alındığında iklim değişikliğinin farklı sonuçları ile medyada yer alıp almadığı, nasıl ve ne kadar yer aldığı kamuoyunun bilinçlenmesinde, kamusal baskı oluşmasında, konunun politik mecralarda tartışılmasında etkilidir. Aynı şekilde iklim değişikliğinin toplum, çevre, nüfus, ekonomi ve benzerine etkilerinin bilimsel olarak tartışılması, akademik kamuoyunda paylaşılması da, politika yapıcıların, sivil toplum örgütlerinin konuya dikkatlerinin akademik düzeyde çekilmesinde önemlidir. Bu çalışmanın amacı iklim değişikliğinin yarattığı sonuçların medyada yer alma biçimlerini sorunsallaştıran akademik metinleri analiz etmektir. Böylelikle iklim değişikliğinin medyada (televizyon, online/basılı gazeteler, sosyal medya, kitaplar, reklamlar, filmler, diziler vb.) nasıl ele alındığını sorunsallaştıran bilimsel çalışmalar bibliyografik olarak analiz edilerek iletişim çalışmaları alanındaki akademik ilginin haritası çıkarılacak ve saptamalarda bulunulacaktır.
AtıfOpenAlex9TR Dizin7İncele - MakaleAçık Erişim2024İkiz dönüşüm kapsamında kritik hammadde ve teknolojilerinin zaman serileri analizi
Göçmen, Elifcan; Say, Halise Miray; Öner, Mahir
Kritik hammaddeler, sürdürülebilir ekonomi için oldukça önemli ve tedarikleriyle bağlantılı olarak yüksek risk taşıyan hammaddelerdir. Yeşil dönüşüm ve teknolojinin dönüşümünü birleştiren ikiz dönüşümde kullanılan teknolojilerde kritik hammaddelerin kullanımı, bu hammaddelerin önemini ve arz riskini arttırmaktadır. Bu kapsamda, ülkede bu dönüşüme zemin hazırlayan teknolojilerin, uygulamaların ve politikaların incelenmesi ve zaman serileri analiziyle incelenmesi amaçlanmaktadır. Yeşil dönüşüm için ülkemizde önemli potansiyeli bulunan ve kritik hammaddelerin kullanıldığı çoğu teknolojinin de kaynağı olan güneş, rüzgâr, hidrolik, jeotermal, termik enerjinin geçmiş verilerinin analizi ve gelecek projeksiyonu sunulmaktadır. Çalışmada kullanılan zaman serileri analizi yöntemleri, basit hareketli ortalama, ağırlıklı hareketli ortalama ve üstel hareketli ortalama yöntemleridir. Üstel hareketli ortalama, güncel verilere daha fazla ağırlık verdiği için, tahmin başarısı ve hata düzeyleri konusunda daha olumlu sonuçlar vermektedir. Böylelikle, her enerji kaynağının gelecek trendleri, bu enerji kaynaklarıyla bağlantılı teknolojilerde kullanılan hammaddelerin talep düzeyi ve bu hammaddelerin kritikliğini de ortaya koyacaktır.
