A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers
This study evaluates the effectiveness of Recurrent Neural Networks (RNNs) and Transformer-based models in predicting the Air Quality Index (AQI). Accurate AQI prediction is critical for mitigating the significant health impacts of air pollution and plays a vital role in public health protection an...
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| Format: | Article |
| Language: | English |
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Universidad Politécnica Salesiana
2025-01-01
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| Series: | Ingenius: Revista de Ciencia y Tecnología |
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| Online Access: | https://revistas.ups.edu.ec/index.php/ingenius/article/view/9557 |
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| author | Pablo Andrés Buestán Andrade Pedro Esteban Carrión Zamora Anthony Eduardo Chamba Lara Juan Pablo Pazmiño Piedra |
| author_facet | Pablo Andrés Buestán Andrade Pedro Esteban Carrión Zamora Anthony Eduardo Chamba Lara Juan Pablo Pazmiño Piedra |
| author_sort | Pablo Andrés Buestán Andrade |
| collection | DOAJ |
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This study evaluates the effectiveness of Recurrent Neural Networks (RNNs) and Transformer-based models in predicting the Air Quality Index (AQI). Accurate AQI prediction is critical for mitigating the significant health impacts of air pollution and plays a vital role in public health protection and environmental management. The research compares traditional RNN models, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, with advanced Transformer architectures. Data were collected from a weather station in Cuenca, Ecuador, focusing on key pollutants such as CO, NO2, O3, PM2.5, and SO2. Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). The findings reveal that the LSTM model achieved superior performance, with an R2 of 0.701, an RMSE of 0.087, and an MAE of 0.056, demonstrating superior capability in capturing temporal dependencies within complex datasets. Conversely, while Transformer-based models exhibited potential, they were less effective in handling intricate time-series data, resulting in comparatively lower accuracy. These results position the LSTM model as the most reliable approach for AQI prediction, offering an optimal balance between predictive accuracy and computational efficiency. This research contributes to improving AQI forecasting and underscores the importance of timely interventions to mitigate the harmful effects of air pollution.
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| format | Article |
| id | doaj-art-43b9c3e3a5694dff8a8e844d0dc43f46 |
| institution | Kabale University |
| issn | 1390-650X 1390-860X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universidad Politécnica Salesiana |
| record_format | Article |
| series | Ingenius: Revista de Ciencia y Tecnología |
| spelling | doaj-art-43b9c3e3a5694dff8a8e844d0dc43f462025-02-07T16:30:08ZengUniversidad Politécnica SalesianaIngenius: Revista de Ciencia y Tecnología1390-650X1390-860X2025-01-013310.17163/ings.n33.2025.06A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformersPablo Andrés Buestán Andrade0https://orcid.org/0000-0002-9210-1591Pedro Esteban Carrión Zamora1https://orcid.org/0009-0007-4230-6891Anthony Eduardo Chamba Lara2https://orcid.org/0009-0003-1231-1068Juan Pablo Pazmiño Piedra3https://orcid.org/0000-0003-0069-7680Universidad Católica de CuencaUniversidad Católica de CuencaUniversidad Católica de CuencaUniversidad Católica de Cuenca This study evaluates the effectiveness of Recurrent Neural Networks (RNNs) and Transformer-based models in predicting the Air Quality Index (AQI). Accurate AQI prediction is critical for mitigating the significant health impacts of air pollution and plays a vital role in public health protection and environmental management. The research compares traditional RNN models, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, with advanced Transformer architectures. Data were collected from a weather station in Cuenca, Ecuador, focusing on key pollutants such as CO, NO2, O3, PM2.5, and SO2. Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). The findings reveal that the LSTM model achieved superior performance, with an R2 of 0.701, an RMSE of 0.087, and an MAE of 0.056, demonstrating superior capability in capturing temporal dependencies within complex datasets. Conversely, while Transformer-based models exhibited potential, they were less effective in handling intricate time-series data, resulting in comparatively lower accuracy. These results position the LSTM model as the most reliable approach for AQI prediction, offering an optimal balance between predictive accuracy and computational efficiency. This research contributes to improving AQI forecasting and underscores the importance of timely interventions to mitigate the harmful effects of air pollution. https://revistas.ups.edu.ec/index.php/ingenius/article/view/9557Air Quality IndexRNNLSTMTransformersPollution Forecasting |
| spellingShingle | Pablo Andrés Buestán Andrade Pedro Esteban Carrión Zamora Anthony Eduardo Chamba Lara Juan Pablo Pazmiño Piedra A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers Ingenius: Revista de Ciencia y Tecnología Air Quality Index RNN LSTM Transformers Pollution Forecasting |
| title | A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers |
| title_full | A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers |
| title_fullStr | A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers |
| title_full_unstemmed | A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers |
| title_short | A comprehensive evaluation of ai techniques for air quality index prediction: RNNs and transformers |
| title_sort | comprehensive evaluation of ai techniques for air quality index prediction rnns and transformers |
| topic | Air Quality Index RNN LSTM Transformers Pollution Forecasting |
| url | https://revistas.ups.edu.ec/index.php/ingenius/article/view/9557 |
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