Explainable sequence-to-sequence GRU neural network for pollution forecasting
Abstract The goal of pollution forecasting models is to allow the prediction and control of the air quality. Non-linear data-driven approaches based on deep neural networks have been increasingly used in such contexts showing significant improvements w.r.t. more conventional approaches like regressi...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-06-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-023-35963-2 |