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


Yalcin S., AYYILDIZ E.

Environmental Science and Pollution Research, vol.31, no.38, pp.51000-51024, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 38
  • Publication Date: 2024
  • Doi Number: 10.1007/s11356-024-34430-3
  • Journal Name: Environmental Science and Pollution Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, EMBASE, Environment Index, Geobase, MEDLINE, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.51000-51024
  • Keywords: AHP, Artificial intelligence, Interval-valued neutrosophic numbers, SWOT, Wastewater treatment
  • Karadeniz Technical University Affiliated: Yes

Abstract

The 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.