Authors:
Natasha Randall
1
;
Gernot Heisenberg
1
and
Juan Luis Ramirez Duval
2
Affiliations:
1
Institute of Information Science, Technical University of Applied Sciences Cologne, Germany
;
2
Institute for Natural Resources Technology and Management, Technical University of Applied Sciences Cologne, Germany
Keyword(s):
Flood Forecasting, Generative Adversarial Networks, Image Generation, Deep Learning.
Abstract:
Floods are one of the most dangerous, impactful natural disasters, and flood forecasting is a critical component of effective pre-flooding preparedness. In this paper a data-driven approach to flood forecasting is presented, which provides photorealistic predictions that are less computationally expensive to generate than traditional physically-based models. A ‘PairedAttention’ generative adversarial network (GAN) was developed, that combines attention and content mask subnetworks, and was trained on paired sets of pre- and post-flooding aerial satellite images aligned with topographical data. The PairedAttention GAN achieved 88% accuracy and an F1 score of 0.8 at flood predictions on three USA flood events, and an ablation study determined that the digital elevation model was the most significant factor to improving the GAN’s performance. Although the model is a successful proof-of-concept for the effectiveness of a data-driven GAN to generate photorealistic, accurate aerial flood p
rediction imagery, it nevertheless struggled with generalisation, indicating an important avenue for future research.
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