Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Dayi, Faruk(1)
• Erdogdu, Aylin(1)
• Esmer, Yusuf(1)
• Ganji, Farshad(1)
• Yildiz, Ferah(1)
• Çakir, Uğur(1)
• Şahin, Erol(1)

• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/12712
  A Hybrid Deep Learning–Fuzzy–Genetic Framework for Climate-Resilient Agricultural Investment and Resource Allocation Under Carbon Market Uncertainty

  Erdogdu, Aylin; Dayi, Faruk; Yildiz, Ferah; Esmer, Yusuf; Ganji, Farshad

  Climate variability, environmental uncertainty, and carbon-market dynamics increasingly challenge agricultural investment and resource allocation decisions worldwide. This study proposes an integrated hybrid decision-support framework combining Long Short-Term Memory (LSTM) deep learning, Interval Type-2 Fuzzy Logic Systems, and Genetic Algorithms to support climate-resilient agricultural investment analysis under uncertainty. The framework integrates predictive modeling, uncertainty representation, and multi-objective optimization within a unified computational architecture. The empirical analysis was conducted using agricultural, climate, and carbon-market datasets covering Europe, Asia, and Africa over the 2010–2025 period. Agricultural productivity indicators, commodity price variables, climate-risk parameters, and carbon-market data were integrated into the modeling process. LSTM models were employed to analyze temporal agricultural and climate-related dynamics, while Interval Type-2 fuzzy systems were used to represent ambiguity associated with environmental and market uncertainty. Genetic Algorithms were subsequently applied to optimize investment allocation under conflicting objectives related to profitability, sustainability, and risk. The findings suggest that the proposed hybrid framework may improve adaptive investment evaluation and optimization performance under uncertain climate conditions relative to standalone computational approaches within the scope of the analyzed datasets. The results further highlight the importance of integrating predictive analytics, uncertainty modeling, and sustainability-oriented optimization within agricultural decision-support systems. However, the framework should be interpreted as a climate-resilient decision-support architecture rather than a universally deterministic forecasting mechanism. Overall, the study contributes to the emerging literature on agricultural sustainability and climate-resilient investment by presenting a transparent and uncertainty-aware computational framework under evolving environmental and carbon-market conditions.

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  AtıfScopus1·WoS1·OpenAlex2

  İncele
• MakaleAçık Erişim2015Scopus SJRQ1/r/bayburt/12302
  Using solar greenhouses in cold climates and evaluating optimum type according to sizing, position and location: A case study

  Çakir, Uğur; Şahin, Erol

  This study deals with a comparison among five common greenhouse types with regard to total solar radiation gaining rates in greenhouse using season under some assumptions. Additionally, possibility of using greenhouses in cold climate regions is investigated. We analyzed five greenhouse types and developed a script in MATLAB platform for the solar radiation availability. This model is applicable and suitable for other buildings and places on the world. Even-span, uneven-span, vinery, semicircular and elliptic types of greenhouses have been analyzed. A "k" value (ratio of length to width of greenhouse) and greenhouse azimuth angle (GAA) are described. Seven different floor area are assigned for each greenhouse type such as 50m2, 100m2, 150m2, 200m2, 250m2, 300m2 and 400m2, respectively. For each floor area, k value is assigned from 1 to 10. Each greenhouse is oriented in 90 different angles according to south facade. Seasonal total solar energy gaining rates is computed individually for all possible greenhouse types, area, k number and orientation. Then a comprehensive comparison is made to determine the optimal greenhouse. The results show that greenhouses are usable and suitable for using in cold climate regions to increase the productivity. In addition, the elliptic type is the optimum one in all analyzed types of greenhouses for Bayburt conditions for all floor areas. It is followed by uneven-span, even-span, semi-circular and vinery type of greenhouses respectively. Shape and type of the roof are main effective parameters on solar energy gaining rates of greenhouses. Elliptic greenhouse should be preferred on the band of the latitude of Bayburt, unless there is restrictive factor like sizing and terrain.

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  AtıfScopus93·WoS77·OpenAlex95

  İncele