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.
References :
- Pal, S. C., Saha, A., Chowdhuri, I., Ruidas, D., Chakrabortty, R., Roy, P., & Shit, M. (2023). Earthquake hotspot and coldspot: Where, why and how? Geosystems and Geoenvironment, 2(1), 100130. https://doi.org/10.1016/j.geogeo.2022.100130
- Cremen, G., Galasso, C., & Zuccolo, E. (2022). Investigating the potential effectiveness of earthquake early warning across Europe. Nature Communications, 13(1), 1–10. https://doi.org/10.1038/s41467-021-27807-2
- Bossu, R., Finazzi, F., Steed, R., Fallou, L., & Bondár, I. (2022). “shaking in 5 seconds!”—Performance and user appreciation assessment of the earthquake network smartphone-based public earthquake early warning system. Seismological Research Letters, 93(1), 137–148. https://doi. org/10.1785/0220210180
- Shokouhi, P., Girkar, V., Rivière, J., Shreedharan, S., Marone, C., Giles, C. L., & Kifer, D. (2021). Deep learning can predict laboratory quakes from active source seismic data. Geophysical Research Letters, 48(12), e2021GL093187. https://doi.org/10.1029/ 2021gl093187
- Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. Geophysical Journal International, 226(2), 1086–1104. https://doi.org/10.1093/gji/ggab139
- Kumar, S. (2020). Development of earthquake early warning systems for Kachchh, Gujarat, in India using τc and Pd. Arab. J. Geosci.
- Mousavi, S. M., & Beroza, G. C. (2020). A Machine Learning Approach for Earthquake Magnitude Estimation. Geophysical Research Letters, 47(1), e2019GL085976.
- Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer: An attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 11(1), 1-12.
- Zhang, X., Zhang, J., Yuan, C., Liu, S., Chen, Z., & Li, W. (2020). Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method. Scientific Reports, 10(1), 1-12.
- Chin, T.-L., Chen, K.-Y., Chen, D.-Y., & Lin, D.-E. (2020). Intelligent real-time earthquake detection by recurrent neural networks. IEEE Transactions on Geoscience and Remote Sensing, 58(8), 5440-5449.
- Saad, O. M., Hafez, A. G., & Soliman, M. S. (2020). Deep learning approach for earthquake parameters classification in earthquake early warning system. IEEE Geoscience and Remote Sensing Letters.
- Basu, S., Pandit, S., Chakrabarti, A., & Barman, S. (2019, December). FPGA based hardware design for noise suppression and seismic event detection. In Proceedings of 5th IEEE International Symposium on Smart Electronic Systems (iSES) (pp. 1-6). IEEE.
- Chin, T.-L., Huang, C.-Y., Shen, S.-H., Tsai, Y.-C., Hu, Y. H., & Wu, Y.-M. (2019). Learn to detect: Improving the accuracy of earthquake detection. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 8867-8878.
- Kong, Q., Allen, R. M., Schreier, L., & Kwon, Y.-W. (2016). MyShake: A smartphone seismic network for earthquake early warning and beyond. Science Advances, 2(2), e1501055.
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.