A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning

TL;DR

This paper introduces a deep learning framework for predicting environmental data across space and time, effectively modeling complex dependencies and interpolating irregular measurements to reconstruct continuous spatio-temporal fields.

Contribution

The paper presents a novel deep learning-based framework that decomposes spatio-temporal processes for improved environmental data prediction and interpolation.

Findings

Effective modeling of spatio-temporal dependencies

Successful interpolation of irregular environmental measurements

Validated on simulated and real-world data

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.