A Novel Framework for Spatio-Temporal Prediction of Environmental Data
Using Deep Learning
- URL: http://arxiv.org/abs/2007.11836v2
- Date: Tue, 22 Dec 2020 14:46:24 GMT
- Title: A Novel Framework for Spatio-Temporal Prediction of Environmental Data
Using Deep Learning
- Authors: Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski
- Abstract summary: We introduce here a framework for decomposed-temporal prediction of climate and environmental data using deep learning.
Specifically, we introduce functions which can be spatially and mapped on a regular grid allowing the reconstruction of complete-temporal-signal.
Applications on simulated real-world data will show the effectiveness of the proposed framework.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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