Random forest-based multi-hazard loss estimation using hypothetical data at seismic and tsunami monitoring networks
AbstractThis article presents a novel approach to estimate multi-hazard loss in a post-event situation, resulting from cascading earthquake and tsunami events with machine learning for the first time. The proposed methodology combines the power of random forest (RF) with data that are simulated at s...
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| פורמט: | Article |
| שפה: | English |
| יצא לאור: |
Taylor & Francis Group
2023-12-01
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| סדרה: | Geomatics, Natural Hazards & Risk |
| נושאים: | |
| גישה מקוונת: | https://www.tandfonline.com/doi/10.1080/19475705.2023.2275538 |