Autoencoder-Driven Weather Clustering for Source Estimation during Nuclear Events

@article{Klampanos2018AutoencoderDrivenWC,
  title={Autoencoder-Driven Weather Clustering for Source Estimation during Nuclear Events},
  author={Iraklis A. Klampanos and Athanasios Davvetas and Spyros Andronopoulos and Charalambos Pappas and Andreas Ikonomopoulos and Vangelis Karkaletsis},
  journal={ArXiv},
  year={2018},
  volume={abs/1709.05840}
}
Abstract Emergency response applications for nuclear or radiological events can be significantly improved via deep feature learning due its ability to capture the inherent complexity of the data involved. In this paper we present a novel methodology for rapid source estimation during radiological releases based on deep feature extraction and weather clustering. Atmospheric dispersions are then calculated based on identified predominant weather patterns and are matched against simulated… 
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