A novel framework for spatio-temporal prediction of environmental data using deep learning

Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis. Indeed, being universal nonlinear...

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Autores principales:	Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0057d3821f644db5b0eb8fbd457f41e3
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id	oai:doaj.org-article:0057d3821f644db5b0eb8fbd457f41e3
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spelling	oai:doaj.org-article:0057d3821f644db5b0eb8fbd457f41e32021-12-02T11:57:58ZA novel framework for spatio-temporal prediction of environmental data using deep learning10.1038/s41598-020-79148-72045-2322https://doaj.org/article/0057d3821f644db5b0eb8fbd457f41e32020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79148-7https://doaj.org/toc/2045-2322Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis. Indeed, being universal nonlinear function approximation tools, Machine Learning algorithms are efficient in analysing and modelling spatially and temporally variable environmental data. While Deep Learning models have proved to be able to capture spatial, temporal, and spatio-temporal dependencies through their automatic feature representation learning, the problem of the interpolation of continuous spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we show how spatio-temporal processes can be decomposed in terms of a sum of products of temporally referenced basis functions, and of stochastic spatial coefficients which can be spatially modelled and mapped on a regular grid, allowing the reconstruction of the complete spatio-temporal signal. Applications on two case studies based on simulated and real-world data will show the effectiveness of the proposed framework in modelling coherent spatio-temporal fields.Federico AmatoFabian GuignardSylvain RobertMikhail KanevskiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Federico Amato Fabian Guignard Sylvain Robert Mikhail Kanevski A novel framework for spatio-temporal prediction of environmental data using deep learning
description	Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis. Indeed, being universal nonlinear function approximation tools, Machine Learning algorithms are efficient in analysing and modelling spatially and temporally variable environmental data. While Deep Learning models have proved to be able to capture spatial, temporal, and spatio-temporal dependencies through their automatic feature representation learning, the problem of the interpolation of continuous spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we show how spatio-temporal processes can be decomposed in terms of a sum of products of temporally referenced basis functions, and of stochastic spatial coefficients which can be spatially modelled and mapped on a regular grid, allowing the reconstruction of the complete spatio-temporal signal. Applications on two case studies based on simulated and real-world data will show the effectiveness of the proposed framework in modelling coherent spatio-temporal fields.
format	article
author	Federico Amato Fabian Guignard Sylvain Robert Mikhail Kanevski
author_facet	Federico Amato Fabian Guignard Sylvain Robert Mikhail Kanevski
author_sort	Federico Amato
title	A novel framework for spatio-temporal prediction of environmental data using deep learning
title_short	A novel framework for spatio-temporal prediction of environmental data using deep learning
title_full	A novel framework for spatio-temporal prediction of environmental data using deep learning
title_fullStr	A novel framework for spatio-temporal prediction of environmental data using deep learning
title_full_unstemmed	A novel framework for spatio-temporal prediction of environmental data using deep learning
title_sort	novel framework for spatio-temporal prediction of environmental data using deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/0057d3821f644db5b0eb8fbd457f41e3
work_keys_str_mv	AT federicoamato anovelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning AT fabianguignard anovelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning AT sylvainrobert anovelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning AT mikhailkanevski anovelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning AT federicoamato novelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning AT fabianguignard novelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning AT sylvainrobert novelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning AT mikhailkanevski novelframeworkforspatiotemporalpredictionofenvironmentaldatausingdeeplearning
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A novel framework for spatio-temporal prediction of environmental data using deep learning

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