Authors : Vijaya Saraswathi R

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/3dbcwjym

Scribd : https://tinyurl.com/yfk3s52n

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1107

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

Abstract : The most devastating natural disasters on earth are earthquakes that causes long-term effects on geography, civilization, and human life. These unpredictable events pose a serious threat to infrastructure. Furthermore, the current Earthquake Early Warning (EEW) systems are facing issues such as limited warning times, false alarms, maintenance costs, high construction costs, and data interpretation. Highlighting these as an urgent need for mitigation measures, there is a need to improve the effectiveness of electronic alerts and public safety measures. For this transformative machine learning techniques and the integration of disparate data, can embark on creating social security and lives protecting from major environmental disasters like earthquakes. This paper has compared various Machine Learning (ML) techniques by training them by using two datasets: one from India and another from India United States Geological from Research World Database to improve the robustness and generality of the earthquake prediction model in the Earthquake Early Warning (EEW) framework. This represents a major advance for earthquake detection and promises to reduce response time. Among various ML Techniques, Random Forest has performed well in earthquake warning with 96.06% accuracy and 98.6% precision.

Keywords : Earthquake Early Warning System, Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, K-Nearest Neighbors (KNN), Logistic Regression.

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