Enhancing urban air quality prediction using time-based-spatial forecasting framework
Abstract Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83248-z |
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| author | Shrikar Jayaraman Nathezhtha T Abirami S Sakthivel G |
| author_facet | Shrikar Jayaraman Nathezhtha T Abirami S Sakthivel G |
| author_sort | Shrikar Jayaraman |
| collection | DOAJ |
| description | Abstract Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. Simultaneously, time series model, specifically the ARIMA (AutoRegressive Integrated Moving Average) was employed to capture temporal dependencies using pollutant concentration readings over time. The dataset included information such as date, time, pollutant concentrations and AQI was further preprocessed and divided into training and testing sets. The CNN was configured to utilize the normalized latitude and longitude grid, while the ARIMA model concurrently processed the pollutant concentrations. The model was trained on the training dataset, and a 6 hour forecast is generated for each test instance. The outcomes demonstrate the TBS model’s ability to accurately predict AQI values. The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations. |
| format | Article |
| id | doaj-art-89ba07ebdc044dab8e2ef8ddbfc66a4b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-89ba07ebdc044dab8e2ef8ddbfc66a4b2025-02-09T12:31:25ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-024-83248-zEnhancing urban air quality prediction using time-based-spatial forecasting frameworkShrikar Jayaraman0Nathezhtha T1Abirami S2Sakthivel G3Vellore Institute of Technology ChennaiVellore Institute of Technology ChennaiVellore Institute of Technology ChennaiVellore Institute of Technology ChennaiAbstract Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. Simultaneously, time series model, specifically the ARIMA (AutoRegressive Integrated Moving Average) was employed to capture temporal dependencies using pollutant concentration readings over time. The dataset included information such as date, time, pollutant concentrations and AQI was further preprocessed and divided into training and testing sets. The CNN was configured to utilize the normalized latitude and longitude grid, while the ARIMA model concurrently processed the pollutant concentrations. The model was trained on the training dataset, and a 6 hour forecast is generated for each test instance. The outcomes demonstrate the TBS model’s ability to accurately predict AQI values. The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations.https://doi.org/10.1038/s41598-024-83248-zAQITBSCNNARIMASpatial characteristicsForecasting |
| spellingShingle | Shrikar Jayaraman Nathezhtha T Abirami S Sakthivel G Enhancing urban air quality prediction using time-based-spatial forecasting framework Scientific Reports AQI TBS CNN ARIMA Spatial characteristics Forecasting |
| title | Enhancing urban air quality prediction using time-based-spatial forecasting framework |
| title_full | Enhancing urban air quality prediction using time-based-spatial forecasting framework |
| title_fullStr | Enhancing urban air quality prediction using time-based-spatial forecasting framework |
| title_full_unstemmed | Enhancing urban air quality prediction using time-based-spatial forecasting framework |
| title_short | Enhancing urban air quality prediction using time-based-spatial forecasting framework |
| title_sort | enhancing urban air quality prediction using time based spatial forecasting framework |
| topic | AQI TBS CNN ARIMA Spatial characteristics Forecasting |
| url | https://doi.org/10.1038/s41598-024-83248-z |
| work_keys_str_mv | AT shrikarjayaraman enhancingurbanairqualitypredictionusingtimebasedspatialforecastingframework AT nathezhthat enhancingurbanairqualitypredictionusingtimebasedspatialforecastingframework AT abiramis enhancingurbanairqualitypredictionusingtimebasedspatialforecastingframework AT sakthivelg enhancingurbanairqualitypredictionusingtimebasedspatialforecastingframework |