Machine learning models for predicting flood insurance claims through recorded streamflow and historical-claims data integration

Georgios T. Manolis; Konstantinos Papoulakos; Nikolaos Tepetidis; Theano Iliopoulou; Panayiotis Dimitriadis; Dimosthenis Tsaknias; Demetris Koutsoyiannis

doi:https://doi.org/10.5194/egusphere-egu25-4837

[Back] [Session HS3.1]

EGU25-4837, updated on 12 Jan 2026

https://doi.org/10.5194/egusphere-egu25-4837

EGU General Assembly 2025

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine learning models for predicting flood insurance claims through recorded streamflow and historical-claims data integration

Georgios T. Manolis¹, Konstantinos Papoulakos¹, Nikolaos Tepetidis¹, Theano Iliopoulou¹, Panayiotis Dimitriadis¹, Dimosthenis Tsaknias², and Demetris Koutsoyiannis¹

Georgios T. Manolis et al.

¹National Technical University of Athens, School of Civil Engineering, Water Resources and Enviromental Engineering, Athens, Greece (papoulakoskon@gmail.com)
²Independent Researcher, Zografou, Athens, Greece

How to cite: Manolis, G. T., Papoulakos, K., Tepetidis, N., Iliopoulou, T., Dimitriadis, P., Tsaknias, D., and Koutsoyiannis, D.: Machine learning models for predicting flood insurance claims through recorded streamflow and historical-claims data integration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4837, https://doi.org/10.5194/egusphere-egu25-4837, 2025.

This abstract has been withdrawn on 27 Apr 2025.