AI-Driven Solutions for Natural Resource Optimization

Artificial intelligence has become an increasingly influential tool in the field of natural resource management, where complex and interdependent systems such as water, energy, land, and ecosystems are under mounting stress from climate change, population growth, and industrial development. Traditional management approaches, often based on deterministic or simplified models, have struggled to fully capture the dynamic, uncertain, and interconnected nature of these systems. Recent advances in AI and data science offer powerful capabilities for analyzing vast datasets, uncovering patterns, and generating predictive insights that can guide sustainable decision-making. While several studies have demonstrated the potential of machine learning and optimization algorithms in improving efficiency and forecasting, challenges persist in ensuring their interpretability, reliability, and integration with domain-specific knowledge. The field is now moving toward hybrid frameworks that blend AI methods with expert-driven insights to deliver actionable, transparent, and adaptive solutions for resource management.This Research Topic aims to investigate how AI can contribute to more sustainable, efficient, and resilient management of natural resources under increasing environmental and socio-economic pressures. It seeks to unify interdisciplinary research efforts across environmental engineering, computer science, and sustainability science to develop AI-driven approaches that optimize resource allocation, support adaptive planning, and enhance system performance. Specifically, the goal is to identify how AI can bridge the gap between theoretical innovation and operational practice - producing scalable, interpretable, and context-sensitive solutions that contribute to global sustainability and climate adaptation goals.To gather further insights into the integration of artificial intelligence with natural resource systems, this Research Topic welcomes articles focusing on interdisciplinary modeling, algorithm development, and real-world implementation. We encourage submissions that address, but are not limited to, the following themes:• AI applications for water resource management, including flood and drought management and forecasting • Smart irrigation, precision agriculture, and sustainable land-use optimization • AI-driven strategies for renewable energy systems and resource efficiency • AI-driven strategies for environmental monitoring and ecosystem management • Integration of AI with remote sensing, GIS, and real-time data systems • Optimization of resource allocation and system performance under real-world constraints • Combining AI data-driven methods with established engineering and environmental practicesSubmissions should present sound methods, clear insights, and practical relevance to real-world resource management challenges under changing environmental conditions. Topic editors: Ahmed Elbeltagi, Ghufran Ahmed Pasha Source: https://www.frontiersin.org/research-topics/81292/ai-driven-solutions-for-natural-resource-optimization