Big Data Analytics for Planetary Systems: AI-Enhanced Geoinformatics and Quantum Models for Predictive Geophysical Risk Assessment

Abstract: This study addresses the need for decision-grade prediction of geophysical hazards across planetary systems by unifying big-data analytics, AI-enhanced geoinformatics, and quantum models. Conceptually, the work advances a physics-consistent view of spatio-temporal extremes—earthquakes, landslides, volcanic unrest, hydrometeorological events, cryosphere failure, and space-weather—within a comparative planetary context. A framework is developed that integrates multimodal remote-sensing and in situ streams with physics-informed learning and uncertainty quantification, designed for scalability, interpretability, and reproducibility. The central problem targeted is the fragmentation between purely statistical learners (limited physical fidelity) and purely mechanistic models (computationally prohibitive at scale) under nonstationarity and label scarcity. Methodologically, the pipeline fuses optical/SAR/hyperspectral/LiDAR, GNSS, gravimetry, magnetometry, and seismology via graph-based spatio-temporal models and PDE-constrained neural surrogates, augmented by analog-aware transfer learning across planetary bodies. Quantum and quantum-inspired optimization modules accelerate combinatorial subroutines (inversion, sensor placement, probabilistic sampling). Evaluation across representative hazards employs forecast skill and calibration metrics (e.g., Brier score, CRPS, AUC-PR), physical-law residuals, latency, and decision utility under cost–loss analysis. Results indicate statistically significant gains in forecast skill and calibration, reduced physical residuals, and meaningful runtime reductions on bottleneck subproblems, with improved robustness under distribution shift and data scarcity. The impact is an auditable, uncertainty-aware risk assessment capability suitable for operational agencies and planetary missions, enabling earlier warnings, optimized resource allocation, and policy-relevant transparency consistent with leading international journal standards. Keywords: Big data analytics, planetary systems, AI-enhanced geoinformatics, quantum models, geophysical risk assessment, physics-informed learning, multimodal data fusion, uncertainty quantification, transfer learning, graph neural operators, data assimilation, remote sensing, spatio-temporal modeling, probabilistic forecasting, disaster risk reduction.