AI-Driven Predictive Modeling for Real-Time Seismic Activity Monitoring and Earthquake Risk Assessment

Abstract

Real-time seismic activity monitoring and earthquake risk assessment have long been critical aspects of disaster mitigation and urban resilience planning. Traditional models based on statistical or geophysical methods often fail to capture the complexity and dynamic nature of seismic phenomena. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized predictive modeling by enabling data-driven approaches for seismic signal analysis, ground motion prediction, and structural vulnerability assessment. This review paper presents a comprehensive synthesis of recent advancements in AI-driven seismic risk models, emphasizing their capacity for real-time forecasting and rapid decision-making. We examine state-of-the-art frameworks that integrate heterogeneous datasets—including geospatial, sensor-based, and historical seismic data—for enhanced situational awareness. The review also identifies the methodological strengths, comparative efficiencies, and current limitations of various AI techniques in earthquake risk analysis. Moreover, we highlight significant research gaps, such as model interpretability and integration with IoT-based monitoring systems, while discussing challenges like data sparsity, model generalization, and real-time computational constraints. A detailed comparative study and summary of recent works provide clarity on prevailing trends. We conclude by outlining future research directions involving hybrid AI models, high-resolution sensing, and multi-hazard frameworks. This paper serves as a valuable resource for researchers and policymakers focused on advancing earthquake preparations using cutting-edge AI innovations.