Authors : Nimra Jabeen; M. M. Harshitha; Dr. Girish Kumar D.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/4y58ebax

Scribd : https://tinyurl.com/23496c7d

DOI : https://doi.org/10.38124/ijisrt/26apr1414

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study presents QuickVision, an innovative deep learning tool developed for earthquake prediction. QuickVision uses LSTM networks to analyze historical earthquake data and identify patterns and preliminary indicators that could appear before major seismic events. QuickVision looks at how data changes over time to gain better insights, and the model performs more effectively than traditional rule-based methods. The researchers meticulously prepared and modeled the dataset, then conducted a series of experiments to evaluate the system’s effectiveness. Their findings demonstrate that QuickVision excels at detecting early warnings and unusual seismic activity, making it a promising tool for helping communities prepare for earthquakes and reduce associated risks. Consequently, the proposed approach may enable safer and better-prepared regions. safer, better-prepared regions in areas prone to seismic hazards.

Keywords : Earthquake Prediction; Deep Learning; Attention Mechanism; LSTM; Seismic Anomaly Detection.

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