A HYBRID ARTIFICIAL INTELLIGENCE FRAMEWORK FOR SUSTAINABLE DEVELOPMENT DECISION SUPPORT
DOI:
https://doi.org/10.5281/zenodo.22742209Abstract
As environmental challenges get worse, we need tools that assist us make informed, flexible, and unambiguous decisions. A lot of people are starting to believe that AI could be a great assist in reaching the SDGs (Sustainable Development Goals). On the other hand, a new study shows that some policies, how they are carried out, and not combining policies are negative for the environment. This article analyzes recent studies on AI-driven systems for decision-making in sustainable development, focusing on machine learning, fuzzy systems, multi-criteria decision-making (MCDM), evolutionary optimization, and hybrid intelligent architectures. This study contends that singular AI methodologies are inherently inadequate for addressing multi-objective sustainability trade-offs characterized by uncertainty, data heterogeneity, and ethical constraints, as demonstrated by a comparative analysis of seminal publications from 2015 to 2024. The evaluation uncovers issues with concepts and methodologies, particularly for explainability, compliance with governance, and scalability to other domains. One way to solve this challenge is to utilize a layered hybrid AI system with parts for open governance, predictive modeling, figuring out what to do when you don't know what to do, and optimization algorithms. The study shows that future systems for making decisions that are good for the environment shouldn't only use predictive algorithms. They should employ hybrid frameworks that operate well with technology and are good for people and the environment.
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