AI-Driven Predictive Models for Future Sustainability Initiatives

DOI

10.2991/978-94-6463-718-2_9How to use a DOI?

Keywords

AI-driven sustainability; predictive models; deep learning; reinforcement learning; generative AI; sustainable development; real-time monitoring; adaptive decision-making; energy optimization; climate resilience; environmental conservation; resource management; smart cities; AI-powered decision support; scalable AI frameworks

Abstract

Artificial intelligence (AI) is a rapidly evolving field that opens new doors for advancing sustainability initiatives. But, the current AI sustainability models are narrower in focus, tend to be domain-specific, lack a scaling aspect to them, and often a real-world implementation context. We present an original cross-platform AI-powered predictive model to motivate all the parts of our world to achieve future sustainability in terms of energy efficiency, ecological balance, urbanization viability, disposal strategy and black-market control systems, etc. It employs advanced AI technologies like deep learning, reinforcement learning, and generative AI to create predictive and adaptive sustainability models. Distinct from the existing design methodologies emphasizing early-stage design or single-variable optimization, this framework integrates real-time monitoring, adaptive decision-making, and multi-dimensional sustainability assessments. Moreover, we introduce an artificial intelligence-powered decision support system to help policymakers, industries, and researchers effectively execute data-driven sustainable practices. A model that balances ecological goals with economic feasibility while ensuring operational efficiency, breaks traditional trade-off paths of software-based sustainability in AI. The framework is validated using real-world case studies and large-scale datasets, confirming its utility across a range of global sustainability challenges. This study helps in providing the gaps in current research in AI based sustainability solutions and is a building block for intelligent, scalable and sustainable environmental impact management.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Murali Malempati
AU  - V. T. Krishnaprasath
AU  - P. Shanmuga Raja
AU  - Subhashree Darshana
AU  - Karthik Chava
AU  - T. Suresh
PY  - 2025
DA  - 2025/05/23
TI  - AI-Driven Predictive Models for Future Sustainability Initiatives
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 86
EP  - 99
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_9
DO  - 10.2991/978-94-6463-718-2_9
ID  - Malempati2025
ER  -