Spatiotemporal Graph Convolutional Network-Based Long Short-Term Memory Model with A* Search Path Navigation and Explainable Artificial Intelligence for Carbon Monoxide Prediction in Northern Cape Province, South Africa
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| Publicado en: | Atmosphere vol. 16, no. 9 (2025), p. 1107-1136 |
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MDPI AG
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| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2073-4433 | ||
| 024 | 7 | |a 10.3390/atmos16091107 |2 doi | |
| 035 | |a 3254466206 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231428 |2 nlm | ||
| 100 | 1 | |a Agbehadji Israel Edem |u Centre for Global Change, Faculty of Natural and Applied Sciences, Sol Plaatje University, Kimberley 8301, South Africa | |
| 245 | 1 | |a Spatiotemporal Graph Convolutional Network-Based Long Short-Term Memory Model with A* Search Path Navigation and Explainable Artificial Intelligence for Carbon Monoxide Prediction in Northern Cape Province, South Africa | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in issues such as a rise in sea levels, among other things. Identifying road network routes within Northern Cape Province in South Africa that are less exposed to air pollutants like carbon monoxide is the issue this study seeks to address. Methods: The method used for our predictions is based on a graph convolutional network (GCN) and long short-term memory (LSTM). The GCN extracts geospatial characteristics, and the LSTM captures both nonlinear relationships and temporal dependencies in an air pollutant and meteorological dataset. Furthermore, an A* search strategy identifies the path from one location to another with the lowest carbon monoxide concentrations within a road network. The explainable artificial intelligence (xAI) technique is used to describe the nonlinear relationship between the target variable and features. Meteorological and air pollutant data in the form of statistical mean, minimum, and maximum values were leveraged, and a random sampling technique was utilized to fill the data gap to help train the predictive model (GCN-LSTM-A*). Results: The predictive model was evaluated with mean squared error (MSE) and root mean squared error (RMSE) values within two multi-time steps (8 and 16 h) with MSEs of 0.1648 and 0.1701, respectively. The LIME technique, which provides explanations of features, shows that Wind_speed and NO2 and NOx concentrations decreased the predicted CO, whereas PM2.5, PM10, relative humidity, and O3 increased the predicted CO of the route. | |
| 651 | 4 | |a Karoo | |
| 651 | 4 | |a South Africa | |
| 653 | |a Global warming | ||
| 653 | |a Nitrogen dioxide | ||
| 653 | |a Capes (landforms) | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Navigation | ||
| 653 | |a Fossil fuels | ||
| 653 | |a Vandalism | ||
| 653 | |a Air pollution | ||
| 653 | |a Sea level rise | ||
| 653 | |a Long short-term memory | ||
| 653 | |a Random sampling | ||
| 653 | |a Sampling techniques | ||
| 653 | |a Prediction models | ||
| 653 | |a Relative humidity | ||
| 653 | |a Cognition & reasoning | ||
| 653 | |a Carbon monoxide | ||
| 653 | |a Data integrity | ||
| 653 | |a Air | ||
| 653 | |a Search methods | ||
| 653 | |a Statistical sampling | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Particulate matter | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Nitrogen compounds | ||
| 653 | |a Pollutants | ||
| 653 | |a Relocation | ||
| 653 | |a Deep learning | ||
| 653 | |a Climate and human activity | ||
| 653 | |a Trends | ||
| 653 | |a Climate change | ||
| 653 | |a Navigation systems | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Machine learning | ||
| 653 | |a Roads & highways | ||
| 653 | |a Neural networks | ||
| 653 | |a Weather patterns | ||
| 653 | |a Literature reviews | ||
| 700 | 1 | |a Christiana, Obagbuwa Ibidun |u Department of Computer Science and Information Technology, Faculty of Natural and Applied Sciences, Sol Plaatje University, Kimberly 8301, South Africa; ibidun.obagbuwa@spu.ac.za | |
| 773 | 0 | |t Atmosphere |g vol. 16, no. 9 (2025), p. 1107-1136 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254466206/abstract/embedded/09EF48XIB41FVQI7?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254466206/fulltextwithgraphics/embedded/09EF48XIB41FVQI7?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254466206/fulltextPDF/embedded/09EF48XIB41FVQI7?source=fedsrch |