Classification of Tsunami Warning Level using Artificial Neural Network and its Comparison in Southern Java Region
The southern area of Java Island has a significant potential for tsunami events due to its position close to the ring of fire and the megathrust tsunami potential of its seismic gap study. A tsunami warning based on its category has become a solid standard in Tsunami Community Preparedness for Tsuna...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item PeerReviewed |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/283334/1/Classification_of_Tsunami_Warning_Level_using_Artificial_Neural_Network_and_its_Comparison_in_Southern_Java_Region.pdf https://repository.ugm.ac.id/283334/ https://ieeexplore.ieee.org/document/10052907 |
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| Institution: | Universitas Gadjah Mada |
| Language: | English |
| Summary: | The southern area of Java Island has a significant potential for tsunami events due to its position close to the ring of fire and the megathrust tsunami potential of its seismic gap study. A tsunami warning based on its category has become a solid standard in Tsunami Community Preparedness for Tsunami Disaster Prevention. The widely accepted method for providing such Tsunami Warning Information is a non-linear tsunami simulation that requires a vast computation resource. In contrast, the tsunami travel time to the coast is short. Hence, the available time to issue the warning is minimal. This study aims to introduce the Artificial Neural Network (ANN) to alternately classify the Tsunami Warning Level in the Southern Area of Java Island with the case study using the Hypothetical Earthquake of Java Megathrust (Mw 6.0-8.8). The ANN applied to one of the models with the highest test accuracy picked from the available configuration model, combining two or six hidden layers, four activation functions, and two epochs. The investigations showed that 6HL-Leaky-RELU-18E is the chosen configuration model and had an excellent performance with a validation accuracy of 100%; and Precision, Recall, F1 Score of 1.00, 0.79, 0.87, respectively. When tested with the external dataset, it generated a test accuracy of 82 %, with 91.67% of site/tsunami warning status correctly predicted. |
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