Rapid natural hazard extent estimation from twitter data: investigation for hurricane impact areas

Janine Florath (), Jocelyn Chanussot () and Sina Keller ()
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Janine Florath: Karlsruhe Institute of Technology
Jocelyn Chanussot: Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK
Sina Keller: Karlsruhe Institute of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 7, No 36, 6775-6796

Abstract: Abstract Natural hazards have occurred more frequently in the past years and pose a severe risk to human life. Their extents and, thereby, the most heavily affected areas must be estimated at the earliest to limit damages or initiate rescue services. For such estimations, a widely available data source, which is comparatively responsive to short-time changes, is needed and provided by volunteered geographic information (VGI) data. Tropical cyclones are natural hazard events that can cause enormous spatially extended damage. In this study, we introduce Machine Learning approaches such as Extremely Randomized Tree (ET) and Geographically Weighted Regression for estimating hurricane-impacted regions from VGI data. In addition to the general approximate track extent estimation, we also evaluate the possibilities of temporal estimation of track development from VGI data. Different scenarios are evaluated, and we find that the results mainly depend on the choice of the geographical splits for training and test data for the underlying regression task. Suitable splits lead to $$R^2$$ R 2 of 99% in the best cases with the ET model. The estimation results are satisfying when considering the temporal aspect and represent a use-case scenario. Such a combination of Machine Learning approaches and VGI is a simple and fast approach for early natural hazard estimation.

Keywords: Machine learning; Spatial analysis; Temporal development; Estimation framework; Volunteered geographic Information data; Extremely randomized tree (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06488-2

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