Analyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis

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dc.contributor.authorYalcin, Selin
dc.contributor.authorAyyildiz, Ertugrul
dc.date.accessioned2025-03-09T10:57:31Z
dc.date.available2025-03-09T10:57:31Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThe escalating global challenges of population growth, climate crisis, and resource depletion have intensified water scarcity, emphasizing the critical role of wastewater treatment (WWT) in environmental preservation. While discharging untreated wastewater poses extinction risks to various species, effective WWT operations are indispensable for ecosystem continuity and sustainable water sources. Recognizing the complexity of WWT management, this study delves into the potential of artificial intelligence (AI) in strategic planning and decision-making within the WWT domain. Through a comprehensive SWOT analysis, this study evaluates the strengths, weaknesses, opportunities, and threats associated with AI integration in WWT processes. Utilizing the SWOT analysis framework, key criteria are identified, and their importance weights are assessed via the interval-valued neutrosophic analytical hierarchy process (IVN-AHP). According to analysis, the strengths in WWT are crucial, but potential opportunities and threats should not be ignored. The results of the study highlight several key findings regarding the integration of AI in WWT processes. While concerns about the reduction in human resources and potential unemployment, as well as the activation time and high energy consumption of AI systems, are identified as significant challenges, the study underscores the success of AI in data analytics as a strong aspect. Specifically, advanced data analysis techniques and the ability to proactively prevent problems emerge as important strengths of AI in WWT. WWT operators and practitioners are encouraged to prioritize the adoption of advanced data analysis techniques and proactive problem-solving strategies to maximize the effectiveness of AI integration in WWT processes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
dc.description.sponsorshipKaradeniz Teknik Üniversitesi, KTU, (FBA-2023–10983); Karadeniz Teknik Üniversitesi, KTU
dc.identifier.doi10.1007/s11356-024-34430-3
dc.identifier.endpage51024
dc.identifier.issn0944-1344
dc.identifier.issue38
dc.identifier.pmid39106015
dc.identifier.scopus2-s2.0-85200599492
dc.identifier.scopusqualityQ1
dc.identifier.startpage51000
dc.identifier.urihttps://doi.org/10.1007/s11356-024-34430-3
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4889
dc.identifier.volume31
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Science and Pollution Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250310
dc.subjectAHP
dc.subjectArtificial intelligence
dc.subjectInterval-valued neutrosophic numbers
dc.subjectSWOT
dc.subjectWastewater treatment
dc.titleAnalyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis
dc.typeArticle

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