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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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10792884/ |
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| author | Yuvaraja Boddu A. Manimaran B. Arunkumar M. Sucharitha J. Suresh Babu |
| author_facet | Yuvaraja Boddu A. Manimaran B. Arunkumar M. Sucharitha J. Suresh Babu |
| author_sort | Yuvaraja Boddu |
| collection | DOAJ |
| description | 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 Seasonal-Trend decomposition using Loess (STL), the IRSA-TCN model effectively captures intricate spatial and temporal patterns inherent in environmental data. The model demonstrates significant improvements in predictive accuracy, achieving reductions of 10-15% in error metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) compared to traditional forecasting methods. Specifically, the IRSA-TCN model excels in predicting key pollutants, including nitrogen dioxide (NO2), carbon monoxide (CO), benzene, and nitrogen oxides (NOx), showcasing its capability to account for seasonal variations and complex interdependencies among pollutants. The findings underscore the model’s potential as a robust tool for environmental monitoring and management, providing actionable insights for policymakers and stakeholders. Future research will focus on enhancing the model’s adaptability through multi-scale spatial attention mechanisms and exploring hybrid architectures with Graph Neural Networks (GNNs) to further refine its applicability in real-time air quality forecasting scenarios. This work significantly contributes to the academic discourse on advanced analytical techniques in environmental science. |
| format | Article |
| id | doaj-art-75dd76e5c597438683fd818abd93b516 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-75dd76e5c597438683fd818abd93b5162025-02-12T00:01:13ZengIEEEIEEE Access2169-35362024-01-011218923318925210.1109/ACCESS.2024.351509510792884Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend DecompositionYuvaraja Boddu0https://orcid.org/0009-0008-1359-8175A. Manimaran1https://orcid.org/0000-0002-5671-9466B. Arunkumar2https://orcid.org/0000-0001-9490-8818M. Sucharitha3https://orcid.org/0000-0002-2744-4117J. Suresh Babu4https://orcid.org/0009-0007-4915-9898Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Information Technology, Manipal Academy of Higher Education, Manipal Institute of Technology Bengaluru, Manipal, IndiaSchool of Electronics Engineering (SENSE), VIT-AP University, Amaravati, Andhra Pradesh, IndiaDepartment of Computer Applications, Mohan Babu University, Tirupati, Andhra Pradesh, IndiaThis 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 Seasonal-Trend decomposition using Loess (STL), the IRSA-TCN model effectively captures intricate spatial and temporal patterns inherent in environmental data. The model demonstrates significant improvements in predictive accuracy, achieving reductions of 10-15% in error metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) compared to traditional forecasting methods. Specifically, the IRSA-TCN model excels in predicting key pollutants, including nitrogen dioxide (NO2), carbon monoxide (CO), benzene, and nitrogen oxides (NOx), showcasing its capability to account for seasonal variations and complex interdependencies among pollutants. The findings underscore the model’s potential as a robust tool for environmental monitoring and management, providing actionable insights for policymakers and stakeholders. Future research will focus on enhancing the model’s adaptability through multi-scale spatial attention mechanisms and exploring hybrid architectures with Graph Neural Networks (GNNs) to further refine its applicability in real-time air quality forecasting scenarios. This work significantly contributes to the academic discourse on advanced analytical techniques in environmental science.https://ieeexplore.ieee.org/document/10792884/Air qualityimproved residual spatial attentiontemporal convolutional networkseasonal-trend decomposition with loessreptile search algorithm |
| spellingShingle | Yuvaraja Boddu A. Manimaran B. Arunkumar M. Sucharitha J. Suresh Babu Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition IEEE Access Air quality improved residual spatial attention temporal convolutional network seasonal-trend decomposition with loess reptile search algorithm |
| title | Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition |
| title_full | Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition |
| title_fullStr | Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition |
| title_full_unstemmed | Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition |
| title_short | Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition |
| title_sort | advanced air quality forecasting using an enhanced temporal attention driven graph convolutional long short term memory model with seasonal trend decomposition |
| topic | Air quality improved residual spatial attention temporal convolutional network seasonal-trend decomposition with loess reptile search algorithm |
| url | https://ieeexplore.ieee.org/document/10792884/ |
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