Bibliographic Details
| Title: |
Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm. |
| Authors: |
Juanara, Elmo1 elmo.juanara@jaist.ac.jp, Chi Yung Lam cylam@jaist.ac.jp |
| Source: |
Journal of Information Systems Engineering & Business Intelligence. Oct2024, Vol. 10 Issue 3, p378-391. 14p. |
| Subject Terms: |
*Decision trees, *Technological innovations, Tsunamis, Technology assessment, Digital technology |
| Abstract: |
Background: Tsunami caused by volcanic collapse are categorized as non-seismic uncommon events, unlike tsunamis caused by earthquakes, which are common events. The traditional tsunami early warning based on the seismic sensor (e.g. earthquake detectors) may not be applicable to volcanic tsunamis because they do not generate seismic waves. Consequently, these tsunamis cannot be detected in advance, and warnings cannot be issued. New methods should be explored to address these non-seismic tsunamis caused by volcanic collapse. Objective: This study explored the potential of machine learning algorithms in supporting early warning level issuing for nonseismic tsunamis, specifically volcanic tsunamis. The Anak Krakatau volcano event in Indonesia was used as a case study. Methods: This study generated a database of 160 collapse scenarios using numerical simulation as input sequences. A classification model was constructed by defining the worst tsunami elevation and its arrival time at the coast. The database was supervised by labeling the warning levels as targets. Subsequently, a decision tree algorithm was employed to classify the warning levels. Results: The results demonstrated that the classification model performs very well for the Major Tsunami, Minor Tsunami, and Tsunami classes, achieving high precision, recall, and F1-Score with very high accuracy of 98%. However, the macro average indicates uneven performance across classes, as there are instances of 'No Warning' in some coastal gauges. Conclusion: To improve the model performance in the 'No Warning' class, it is necessary to balance the dataset by including more 'No Warning' scenarios, which can be achieved by simulating additional scenarios involving very small-volume collapse. Additionally, exploring additional collapse parameters such as dip angle and outlier volume could contribute to developing a more robust classification model. [ABSTRACT FROM AUTHOR] |
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| Database: |
Business Source Complete |
