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Frost forecasting model using graph neural networks with spatio-temporal attention

Abstract : Frost forecast is an important issue in climate research because of its economic impact in several industries. In this study, a graph neural network (GNN) with spatio-temporal architecture is proposed to predict minimum temperatures in an experimental site. The model considers spatial and temporal relations and processes multiple time series simultaneously. Performing predictions of 6, 12, and 24 hrs this model outperforms statistical and non-graph deep learning models.
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https://hal.inria.fr/hal-03259658
Contributor : Hernan Lira Connect in order to contact the contributor
Submitted on : Tuesday, June 15, 2021 - 12:52:59 PM
Last modification on : Friday, January 21, 2022 - 3:10:05 AM
Long-term archiving on: : Thursday, September 16, 2021 - 6:04:13 PM

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  • HAL Id : hal-03259658, version 1

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Hernan Lira, Luis Martí, Nayat Sanchez-Pi. Frost forecasting model using graph neural networks with spatio-temporal attention. AI: Modeling Oceans and Climate Change Workshop at ICLR 2021, Nayat Sanchez-Pi; Luis Martí, May 2021, Santiago, Chile. ⟨hal-03259658⟩

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