📅 19 September 2024
DOI: 10.26877/asset.v6i4.1003
Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction
Advance Sustainable Science, Engineering and Technology
Universitas Persatuan Guru Republik Indonesia Semarang
📄 Abstract
Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.
🔖 Keywords
#Complex Network Analysis; Sequential Pattern Mining; Sequential Rule Mining; Centrality Measurement; Minimum Support
ℹ️ Informasi Publikasi
Tanggal Publikasi
19 September 2024
Volume / Nomor / Tahun
Volume 6, Nomor 4, Tahun 2024
📝 HOW TO CITE
Henri Tantyoko; Nurjanah, Dade; Rusmawati, Yanti, "Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction," Advance Sustainable Science, Engineering and Technology, vol. 6, no. 4, Sep. 2024.
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