ArticleScientific reports2020
A novel framework for spatio-temporal prediction of environmental data using deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
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- High-Precision Spatial Interpolation of Meteorological Variables in Complex Terrain Using Machine Learning Methods.Sensors (Basel, Switzerland) · 2026Article
- Recent Advances in Decoupling Strategies for Soft Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Spatial and temporal evolution characteristics of apple carbon emissions in China, 1991-2020.Scientific reports · 2025Article
- Multi-Source Data and Knowledge Fusion via Deep Learning for Dynamical Systems: Applications to Spatiotemporal Cardiac Modeling.IISE transactions on healthcare systems engineering · 2025Article
- Scalable spatiotemporal prediction with Bayesian neural fields.Nature communications · 2024Article
- A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators.Scientific reports · 2024Article
- Multiple Representations in geospatial databases, the brain's spatial cells, and deep learning algorithms.Cartography and geographic information science · 2024Article
- An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data.Urban informatics · 2022Article
- Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential.Stochastic environmental research and risk assessment : research journal · 2022Article
- Exploring the potential of machine learning for simulations of urban ozone variability.Scientific reports · 2021Article
- Dual Memory LSTM with Dual Attention Neural Network for Spatiotemporal Prediction.Sensors (Basel, Switzerland) · 2021Article
- EDISON: An Edge-Native Method and Architecture for Distributed Interpolation.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.