Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm
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 bec...
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Universitas Airlangga
2024-10-01
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| Series: | Journal of Information Systems Engineering and Business Intelligence |
| Online Access: | https://e-journal.unair.ac.id/JISEBI/article/view/60262 |
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| author | Elmo Juanara Chi Yung Lam |
| author_facet | Elmo Juanara Chi Yung Lam |
| author_sort | Elmo Juanara |
| collection | DOAJ |
| description | 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 non-seismic 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.
Keywords: Machine Learning, Classification, Volcanic Tsunamis, Early Warning, Decision Tree |
| format | Article |
| id | doaj-art-b9afb60c2e984de49940d6ba03a9f103 |
| institution | Kabale University |
| issn | 2598-6333 2443-2555 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Universitas Airlangga |
| record_format | Article |
| series | Journal of Information Systems Engineering and Business Intelligence |
| spelling | doaj-art-b9afb60c2e984de49940d6ba03a9f1032025-01-09T07:57:33ZengUniversitas AirlanggaJournal of Information Systems Engineering and Business Intelligence2598-63332443-25552024-10-0110337839110.20473/jisebi.10.3.378-39158428Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree AlgorithmElmo Juanara0https://orcid.org/0000-0003-1224-2405Chi Yung Lam1https://orcid.org/0000-0003-2124-2386Japan Advanced Institute of Science and Technology, Nomi, Japan Japan Advanced Institute of Science and Technology, Nomi, Japan 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 non-seismic 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. Keywords: Machine Learning, Classification, Volcanic Tsunamis, Early Warning, Decision Treehttps://e-journal.unair.ac.id/JISEBI/article/view/60262 |
| spellingShingle | Elmo Juanara Chi Yung Lam Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm Journal of Information Systems Engineering and Business Intelligence |
| title | Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm |
| title_full | Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm |
| title_fullStr | Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm |
| title_full_unstemmed | Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm |
| title_short | Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm |
| title_sort | classification of non seismic tsunami early warning level using decision tree algorithm |
| url | https://e-journal.unair.ac.id/JISEBI/article/view/60262 |
| work_keys_str_mv | AT elmojuanara classificationofnonseismictsunamiearlywarninglevelusingdecisiontreealgorithm AT chiyunglam classificationofnonseismictsunamiearlywarninglevelusingdecisiontreealgorithm |