Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition

Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition

This study presents the Improved Residual Spatial Attention-Temporal Convolutional Network (IRSA-TCN), an advanced framework for enhancing air quality forecasting across multiple pollutants. By integrating Residual Spatial Attention (RSA) with Graph Convolutional Long Short-Term Memory (GCLSTM) and...

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Bibliographic Details
Main Authors: Yuvaraja Boddu, A. Manimaran, B. Arunkumar, M. Sucharitha, J. Suresh Babu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Air quality
improved residual spatial attention
temporal convolutional network
seasonal-trend decomposition with loess
reptile search algorithm
Online Access:https://ieeexplore.ieee.org/document/10792884/
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https://ieeexplore.ieee.org/document/10792884/

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