A Hybrid Deep Learning–Fuzzy–Genetic Framework for Climate-Resilient Agricultural Investment and Resource Allocation Under Carbon Market Uncertainty
Yazarlar (5)
Doç. Dr. Aylin Erdoğdu İstanbul Arel Üniversitesi, Türkiye
Prof. Dr. Faruk DAYI Kastamonu Üniversitesi, Türkiye
Prof. Dr. Ferah Yıldız Kocaeli Üniversitesi, Türkiye
Prof. Dr. Yusuf Esmer Bayburt Üniversitesi, Türkiye
Farshad Ganjı İstanbul Aydın Üniversitesi
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Agriculture (Q1) | ||
| Dergi ISSN | 2077-0472 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2026 |
| Cilt / Sayı / Sayfa | 16 / 11 / 1–27 | DOI | 10.3390/agriculture16111163 |
| Makale Linki | https://doi.org/10.3390/agriculture16111163 | ||
| UAK Araştırma Alanları |
İşletme Finansı
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| Özet |
| 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 … |
| Anahtar Kelimeler |
| sustainable agriculture | deep learning | type-2 fuzzy logic | genetic algorithm | climate risk | carbon markets | multi-objective optimization |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 2 |