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Forecasting global climate drivers using Gaussian processes and convolutional autoencoders
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Donnelly, James, Daneshkhah, Alireza and Abolfathi, Soroush (2024) Forecasting global climate drivers using Gaussian processes and convolutional autoencoders. Engineering Applications of Artificial Intelligence, 128 . 107536. doi:10.1016/j.engappai.2023.107536 ISSN 0952-1976.
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Official URL: https://doi.org/10.1016/j.engappai.2023.107536
Abstract
Machine learning (ML) methods have become an important tool for modelling and forecasting complex high-dimensional spatiotemporal datasets such as those found in environmental and climate modelling applications. ML approaches can offer a fast, low-cost alternative to short-term forecasting than expensive numerical simulation while addressing a significant outstanding limitation of numerical modelling by being able to robustly and dynamically quantify predictive uncertainty. Low-cost and near-instantaneous forecasting of high-level climate variables has clear applications in early warning systems, nowcasting, and parameterising small-scale locally relevant simulations. This paper presents a novel approach for multi-task spatiotemporal regression by combining data-driven autoencoders with Gaussian Processes (GP) to produce a probabilistic tensor-based regression model. The proposed method is demonstrated for forecasting one-step-ahead temperature and pressure on a global scale simultaneously. By conducting probabilistic regression in the learned latent space, samples can be propagated back to the original feature space to produce uncertainty estimates at a vastly reduced computational cost. The composite GP-autoencoder model was able to simultaneously forecast global temperature and pressure values with average errors of 3.82 °C and 638 hPa, respectively. Further, on average the true values were within the proposed posterior distribution 95.6% of the time illustrating that the model produces a well-calibrated predictive posterior distribution.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QC Physics T Technology > TA Engineering (General). Civil engineering (General) T Technology > TC Hydraulic engineering. Ocean engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Gaussian processes, Machine learning, Climatic changes -- Mathematical models, Weather forecasting -- Mathematical models, Artificial intelligence | ||||||||
Journal or Publication Title: | Engineering Applications of Artificial Intelligence | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 0952-1976 | ||||||||
Official Date: | January 2024 | ||||||||
Dates: |
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Volume: | 128 | ||||||||
Article Number: | 107536 | ||||||||
DOI: | 10.1016/j.engappai.2023.107536 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 21 November 2023 | ||||||||
Date of first compliant Open Access: | 22 November 2023 | ||||||||
Open Access Version: |
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