Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation

dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authorscopusid57195629358
dc.authorscopusid57217424609
dc.authorscopusid59125628000
dc.authorwosidAmiri, Zahra/GQQ-6915-2022
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.contributor.authorJafari Navimipour, Nima
dc.contributor.authorHeidari, Arash
dc.contributor.authorNavimipour, Nima Jafari
dc.date.accessioned2024-10-15T19:40:35Z
dc.date.available2024-10-15T19:40:35Z
dc.date.issued2024
dc.departmentKadir Has Universityen_US
dc.department-temp[Amiri, Zahra] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Heidari, Arash] Halic Univ, Dept Software Engn, TR-34060 Istanbul, Turkiye; [Heidari, Arash] Istanbul Atlas Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Kadir Has Univ, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan; [Navimipour, Nima Jafari] Western Caspian Univ, Res Ctr High Technol & Innovat Engn, Baku, Azerbaijanen_US
dc.descriptionHeidari, Arash/0000-0003-4279-8551en_US
dc.description.abstractWith the galloping progress of the changing climates all around the world, Machine Learning (ML) approaches have been prevalently studied in many types of research in this area. ML is a robust tool for acquiring perspectives from data. In this paper, we elaborate on climate change mitigation issues and ML approaches leveraged to solve these issues and aid in the improvement and function of sustainable energy systems. ML has been employed in multiple applications and many scopes of climate subjects such as ecosystems, agriculture, buildings and cities, industry, and transportation. So, a Systematic Literature Review (SLR) is applied to explore and evaluate findings from related research. In this paper, we propose a novel taxonomy of Deep Learning (DL) method applications for climate change mitigation, a comprehensive analysis that has not been conducted before. We evaluated these methods based on critical parameters such as accuracy, scalability, and interpretability and quantitatively compared their results. This analysis provides new insights into the effectiveness and reliability of DL methods in addressing climate change challenges. We classified climate change ML methods into six key customizable groups: ecosystems, industry, buildings and cities, transportation, agriculture, and hybrid applications. Afterward, state-of-the-art research on ML mechanisms and applications for climate change mitigation issues has been highlighted. In addition, many problems and issues related to ML implementation for climate change have been mapped, which are predicted to stimulate more researchers to manage the future disastrous effects of climate change. Based on the findings, most of the papers utilized Python as the most common simulation environment 38.5 % of the time. In addition, most of the methods were analyzed and evaluated in terms of some parameters, namely accuracy, latency, adaptability, and scalability, respectively. Lastly, classification is the most frequent ML task within climate change mitigation, accounting for 40 % of the total. Furthermore, Convolutional Neural Networks (CNNs) are the most widely utilized approach for a variety of applications.en_US
dc.description.woscitationindexScience Citation Index Expanded
dc.identifier.citation0
dc.identifier.doi10.1016/j.energy.2024.132827
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85202946726
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.energy.2024.132827
dc.identifier.urihttps://hdl.handle.net/20.500.12469/6378
dc.identifier.volume308en_US
dc.identifier.wosWOS:001307897800001
dc.identifier.wosqualityQ1
dc.language.isoenen_US
dc.publisherPergamon-elsevier Science Ltden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClimate changeen_US
dc.subjectArtificial intelligenceen_US
dc.subjectSystematic literature reviewen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.titleComprehensive survey of artificial intelligence techniques and strategies for climate change mitigationen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication0fb3c7a0-c005-4e5f-a9ae-bb163df2df8e
relation.isAuthorOfPublication.latestForDiscovery0fb3c7a0-c005-4e5f-a9ae-bb163df2df8e

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