Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review
This systematic review examines the application of artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for climate change adaptation and mitigation in Iran, Pakistan, and Turkey. These three nations—key Economic Cooperation Organization (ECO) members and a nexus bet...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025003548 |
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| author | Muhammad Talha A. Pouyan Nejadhashemi Kieron Moller |
| author_facet | Muhammad Talha A. Pouyan Nejadhashemi Kieron Moller |
| author_sort | Muhammad Talha |
| collection | DOAJ |
| description | This systematic review examines the application of artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for climate change adaptation and mitigation in Iran, Pakistan, and Turkey. These three nations—key Economic Cooperation Organization (ECO) members and a nexus between Europe and South Asia—are experiencing diverse environmental challenges due to varying climatic conditions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search in the Scopus database, ultimately identifying 76 relevant articles out of an initial 492. Although some articles utilized multiple techniques, classical ML methods appeared in approximately 37.3 % of the studies, neural network paradigms in about 57.5 %, and optimization or meta-heuristic algorithms in around 5.0 %. Regarding thematic focus, about 33.3 % of the articles addressed water resource management, 22.2 % focused on climate prediction, 11.1 % on land and agriculture, 9 % on ecosystem modeling, and 24.2 % on natural disaster preparedness and response. The analysis reveals a growing but uneven body of research utilizing AI across the ECO countries. By highlighting successful applications, identifying key gaps—such as limited cross-border collaboration and inconsistent data availability—and proposing a framework for more integrated research, this review aims to guide future initiatives that leverage AI's potential to improve climate resilience and sustainability in the region. |
| format | Article |
| id | doaj-art-2032438573814418b908bf139f98efc7 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-2032438573814418b908bf139f98efc72025-02-02T05:28:41ZengElsevierHeliyon2405-84402025-01-01112e41974Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic reviewMuhammad Talha0A. Pouyan Nejadhashemi1Kieron Moller2Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, 48824, USA; Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USADepartment of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, 48824, USA; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA; Corresponding author. Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, 48824, USA.Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, 48824, USAThis systematic review examines the application of artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for climate change adaptation and mitigation in Iran, Pakistan, and Turkey. These three nations—key Economic Cooperation Organization (ECO) members and a nexus between Europe and South Asia—are experiencing diverse environmental challenges due to varying climatic conditions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search in the Scopus database, ultimately identifying 76 relevant articles out of an initial 492. Although some articles utilized multiple techniques, classical ML methods appeared in approximately 37.3 % of the studies, neural network paradigms in about 57.5 %, and optimization or meta-heuristic algorithms in around 5.0 %. Regarding thematic focus, about 33.3 % of the articles addressed water resource management, 22.2 % focused on climate prediction, 11.1 % on land and agriculture, 9 % on ecosystem modeling, and 24.2 % on natural disaster preparedness and response. The analysis reveals a growing but uneven body of research utilizing AI across the ECO countries. By highlighting successful applications, identifying key gaps—such as limited cross-border collaboration and inconsistent data availability—and proposing a framework for more integrated research, this review aims to guide future initiatives that leverage AI's potential to improve climate resilience and sustainability in the region.http://www.sciencedirect.com/science/article/pii/S2405844025003548Climate changeAdaptation and mitigationArtificial intelligenceMachine learningDeep learning |
| spellingShingle | Muhammad Talha A. Pouyan Nejadhashemi Kieron Moller Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review Heliyon Climate change Adaptation and mitigation Artificial intelligence Machine learning Deep learning |
| title | Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review |
| title_full | Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review |
| title_fullStr | Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review |
| title_full_unstemmed | Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review |
| title_short | Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review |
| title_sort | soft computing paradigm for climate change adaptation and mitigation in iran pakistan and turkey a systematic review |
| topic | Climate change Adaptation and mitigation Artificial intelligence Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025003548 |
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