Bridging Machine Learning and Climate Futures: A Framework for Explainable, Self-Directed (Agentic) AI Models in Monitoring, Mitigation, and Governance of Anthropogenic Climate Impacts

Authors

• Vijayanandh Rajamanickam, Department of Computing and Information Technology, Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus, Trinidad and TobagoFollow
• Ketaki Kulkarni, Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, India
• Vishwanadham Mandala, Service Delivery Lead, Cummins Inc., Columbus, Indiana, USA
• Ozgur Kisi, Ilia State University, Tbilisi, Georgia

Corresponding Author

Vijayanandh Rajamanickam

Subject Area

Computer Science

Article Type

Special Issue Original Study

Abstract

Environmental and societal damage due to anthropogenic climate change demands detection and mitigation strategies that are both effective and accountable. Machine Learning (ML) appears to offer a powerful set of tools but remains unexploited in these areas. A key barrier remains its poor ability to draw causal inferences about external systems, an essential requirement for meaningful continued monitoring, acting on, and shaping of the climate. Explainable, self-directed learned models operate by attributing environmental changes and deciding how best to model the resulting dynamics—they offer a natural solution. Recent developments in computational ecology, meteorology, and ML are combined to propose a framework which utilises available data, semantics, and several properties of self-directed learned models to examine Anthropogenic Climate Change impacts. Three experiments drawn from these fields are presented, together with a research strategy for addressing gaps in current capabilities.

Keywords

Environmental Damage; Machine learning; Computational Ecology; Meteorology; Adaptive Frameworks; Sustainable Solutions

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Rajamanickam, Vijayanandh; Kulkarni, Ketaki; Mandala, Vishwanadham; and Kisi, Ozgur (2026) "Bridging Machine Learning and Climate Futures: A Framework for Explainable, Self-Directed (Agentic) AI Models in Monitoring, Mitigation, and Governance of Anthropogenic Climate Impacts," Mansoura Engineering Journal: Vol. 51 : Iss. 4 , Article 1.
Available at: https://doi.org/10.58491/2735-4202.3406