ArticleScientific reports2024
Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD-GAT-BiLSTM.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Simulation of long-term spatio-temporal environmental dynamics using a unified benchmark of neighbor augmenting, LSTM and graph attention models.Scientific reports · 2026Article
- Forecasting of PMScientific reports · 2026Article
- Explainable spatiotemporal Graph Neural Networks: Multi-City PM2.5 forecasting in Bangladesh with geographic graph topology validation.PloS one · 2026Article
- A multimodal EEG-based psychological risk representation method using spatiotemporal graph attention and adaptive gated fusion.Frontiers in psychiatry · 2026Article
- A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting.Sensors (Basel, Switzerland) · 2025Review
- Air quality prediction-based big data analytics using hebbian concordance and attention-based long short-term memory.Scientific reports · 2025Article
- A hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD-GAT-BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM
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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.