AI-Driven Predictive Modeling of Environmental and Climate Patterns Using Machine Learning and Remote Sensing Data for Sustainable Decision-Making

Abstract

The integration of Artificial Intelligence (particularly machine learning and deep learning) with Geographic Information Systems and remote sensing data has revolutionized predictive modeling of environmental and climate patterns, enabling dynamic, high-resolution analysis for sustainable natural resource management and decision-making. This GeoAI paradigm leverages multi-modal satellite observations (Sentinel-2, Landsat, MODIS), IoT sensors, and advanced architectures such as CNNs for spatial feature extraction, LSTMs for temporal dependencies, Vision Transformers for attention-based modeling, and Physics-Informed Neural Networks (PINNs) to embed physical laws into data-driven predictions. Applications span flood susceptibility mapping, wildfire forecasting, groundwater potential assessment, precision agriculture, forest monitoring, and carbon accounting, often outperforming traditional numerical models in accuracy, speed, and scalability while addressing sub-grid phenomena. Hybrid systems combining physics-based constraints with AI improve interpretability and robustness, supporting real-time early warning, policy formulation, and adaptive strategies. Despite challenges including high computational energy demands, model transferability across regions, and the need for explainable AI, this convergence offers a powerful pathway toward proactive climate resilience, biodiversity conservation, and sustainable development.