Towards a global impact-based forecasting model for tropical cyclones

Abstract ∘ grid resolution by de-aggregation using Google Open Buildings data. Thirdly, we evaluate XGBoost regression models using different combinations of global and local features at grid and municipality spatial levels. We first introduce a two-stage model to predict if the damage is above 10 % and then use a regression model trained on all or only high-damage data. All experiments use data from 39 typhoons that impacted the Philippines between 2006–2020. Due to the scarcity and skewness of the training data, specific attention is paid to data stratification, sampling, and validation techniques. We demonstrate that employing only the global features does not significantly influence model performance. Despite excluding local data on physical vulnerability and storm surge susceptibility, the two-stage model improves upon the municipality-based model with local features. When applied to anticipatory action, our two-stage model would show a higher true-positive rate, a lower false-negative rate, and an improved false-positive rate, implying that fewer resources would be wasted in anticipatory action. We conclude that relying on globally available data sources and working at the grid level holds the potential to render a machine-learning-based impact model generalizable and transferable to locations outside of the Philippines impacted by TCs. Also, a grid-based model increases the resolution of the predictions, which may allow for a more targeted implementation of anticipatory action. However, it should be noted that an impact-based forecasting model can only be as good as the forecast skill of the TC forecast that goes into it. Future research will focus on replicating and testing the approach in other TC-prone countries. Ultimately, a transferable model will facilitate the scaling up of anticipatory action for TCs.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Towards a global impact-based forecasting model for tropical cyclones ; volume:24 ; number:1 ; year:2024 ; pages:309-329 ; extent:21 ; Natural hazards and earth system sciences ; 24, Heft 1 (2024), 309-329 (gesamt 21)

Beteiligte, Orts- und Zeitangaben

• Urheber: Kooshki Forooshani, Mersedeh ; van den Homberg, Marc ; Kalimeri, Kyriaki ; Kaltenbrunner, Andreas ; Mejova, Yelena ; Milano, Leonardo ; Ndirangu, Pauline ; Paolotti, Daniela ; Teklesadik, Aklilu ; Turner, Monica L.

Weitere Informationen

• DOI: 10.5194/nhess-24-309-2024
• URN: urn:nbn:de:101:1-2024020803305511820945
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:38 MESZ