A Scalable Framework for Predictive Environmental Monitoring and Decision Support

This article introduces a comprehensive AI-driven framework designed to transform environmental monitoring and climate resilience through real-time data analysis, predictive modeling, and policy simulation. By integrating satellite imagery, sensor networks, and advanced machine learning techniques, the system addresses critical gaps in current climate monitoring approaches, particularly regarding fragmentation, lack of adaptive feedback loops, and insufficient implementation in vulnerable regions. The framework's modular architecture, comprising data integration, AI analytics, edge computing, and policy visualization components, enables flexible deployment across diverse environmental contexts while maintaining system coherence. Case studies across wildfire prediction, urban cooling optimization, and flood detection demonstrate its practical impact and versatility. Technical innovations, including the Fusion Transformer architecture, ClimateGAN simulations, and bias mitigation strategies, enhance performance while addressing ethical considerations. Comprehensive evaluation confirms significant improvements over existing approaches while maintaining functionality on resource-constrained devices. The framework represents a significant advancement in democratizing access to sophisticated environmental monitoring tools while ensuring algorithmic fairness, responsible deployment in marginalized regions, and transparent decision support for effective climate governance.