Abstract :

Earthquake Early Warning (EEW) Systems Play A Crucial Role In Reducing The Impact Of Seismic Disasters By Providing Advance Alerts Before Strong Ground Shaking Reaches Populated Areas. One Of The Most Critical Components Of An EEW System Is The Rapid And Accurate Estimation Of Earthquake Source Location Immediately After Seismic Activity Is Detected. Traditional Sourcelocation Estimation Methods Often Involve Computationally Intensive Processes That May Limit Warning Speed And Effectiveness. This Paper Presents A Machine Learning-based Framework For Fast And Reliable Source-location Estimation In Earthquake Early Warning Systems. The Proposed Approach Utilizes Seismic Waveform Data Collected From Distributed Sensor Networks And Employs Machine Learning Algorithms To Analyze Seismic Signal Characteristics And Predict Earthquake Epicenter Locations In Real Time. Data Preprocessing, Feature Extraction, And Predictive Modeling Techniques Are Integrated To Improve Estimation Accuracy While Minimizing Computational Delays. Experimental Analysis Demonstrates That Machine Learning Models Significantly Enhance Source-location Estimation Speed And Reliability Compared To Conventional Approaches. The Proposed Framework Contributes To Faster Earthquake Warnings, Improved Disaster Preparedness, And Enhanced Public Safety By Enabling Timely Emergency Response And Risk Mitigation.

Published:

26-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

434-439

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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