Corpus ID: 226306867

Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

@article{Boussioux2020HurricaneFA,
  title={Hurricane Forecasting: A Novel Multimodal Machine Learning Framework},
  author={L. Boussioux and Cynthia Zeng and Th'eo Gu'enais and D. Bertsimas},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.06125}
}

L. Boussioux, Cynthia Zeng, +1 author D. Bertsimas

Published 2020

Computer Science, Physics

ArXiv

This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-feature extractor methodology to mix spatial-temporal… CONTINUE READING

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