A Hybrid Deep Learning-Fuzzy-Genetic Framework for Climate-Resilient Agricultural Investment and Resource Allocation Under Carbon Market Uncertainty

Basit Öğe Kaydı
dc.contributor.authorErdogdu, Aylin
dc.contributor.authorDayi, Faruk
dc.contributor.authorYildiz, Ferah
dc.contributor.authorEsmer, Yusuf
dc.contributor.authorGanji, Farshad
dc.date.accessioned2026-09-01T15:52:34Z
dc.date.available2026-09-01T15:52:34Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractClimate 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.
dc.identifier.doi10.3390/agriculture16111163
dc.identifier.issn2077-0472
dc.identifier.issue11
dc.identifier.scopus2-s2.0-105041395182
dc.identifier.scopusqualityN/A
dc.identifier.urihttp://dx.doi.org/10.3390/agriculture16111163
dc.identifier.urihttps://hdl.handle.net/20.500.12403/8454
dc.identifier.volume16
dc.identifier.wosWOS:001789686900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofAgriculture-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260820
dc.subjectSustainable Agriculture
dc.subjectDeep Learning
dc.subjectType-2 Fuzzy Logic
dc.subjectGenetic Algorithm
dc.subjectClimate Risk
dc.subjectCarbon Markets
dc.subjectMulti-Objective Optimization
dc.titleA Hybrid Deep Learning-Fuzzy-Genetic Framework for Climate-Resilient Agricultural Investment and Resource Allocation Under Carbon Market Uncertainty
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu