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<p>The inherent unpredictability of seismic activity has long impeded the development of precise earthquake forecasting models, rendering traditional statistical methodologies insufficient for mitigating disaster impact. This research presents a novel Deep Learning based Generative framework that synthesizes cloud computing alongside IoT for spatial feature extraction, temporal pattern recognition and augmenting training datasets through synthetic seismic event data generation for low-resource seismic regions. Through the use of empirical seismic multi-modal data and computational models within a cloud hosted infrastructure, the proposed research enabled real-time and scalable earthquake prediction features with valuable tests revealing a crucial 15–25% improved prediction accuracy over the traditional methods, further reducing the significant false positives and improved alert response times. This research redefines the methods of earthquake forecasting creating a stage for a versatile GenAI-based predictive opportunities that can be generalized to broader disaster resilience events, such as tsunami, wildfire and landslide based early warning systems.</p>

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Next-gen earthquake monitoring: leveraging generative AI and deep learning for early warning and response

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Keshav Dhir,
  • Prabhsimran Singh

摘要

The inherent unpredictability of seismic activity has long impeded the development of precise earthquake forecasting models, rendering traditional statistical methodologies insufficient for mitigating disaster impact. This research presents a novel Deep Learning based Generative framework that synthesizes cloud computing alongside IoT for spatial feature extraction, temporal pattern recognition and augmenting training datasets through synthetic seismic event data generation for low-resource seismic regions. Through the use of empirical seismic multi-modal data and computational models within a cloud hosted infrastructure, the proposed research enabled real-time and scalable earthquake prediction features with valuable tests revealing a crucial 15–25% improved prediction accuracy over the traditional methods, further reducing the significant false positives and improved alert response times. This research redefines the methods of earthquake forecasting creating a stage for a versatile GenAI-based predictive opportunities that can be generalized to broader disaster resilience events, such as tsunami, wildfire and landslide based early warning systems.