论文标题
在城市空气质量预测中管理较大的数据集差距：中世纪2022年的DCU-Ingright-AQ
Managing Large Dataset Gaps in Urban Air Quality Prediction: DCU-Insight-AQ at MediaEval 2022
论文作者
论文摘要
计算空气质量指数（AQI）通常使用在固定位置部署的空气质量传感器中的数据流，计算是一个实时过程。如果一个或多个传感器被打破或离线，则无法计算实时AQI值。将来某个点估算AQI值是一个预测过程，并使用历史AQI值来训练和构建模型。在这项工作中，我们专注于填充空气质量数据的差距，在该数据中，任务是在未来的1、5和7天预测AQI。这种情况是一个或多个空气，天气和交通传感器离线的地方，并在这种情况下探讨了预测准确性。这项工作是中世纪2022年城市空气的一部分：DCU-Instright-AQ团队提交的城市生活和空气污染任务，并使用由AQI，天气和CCTV交通图像组成的多模式和跨模式数据进行空气污染预测。
Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI value cannot be computed. Estimating AQI values for some point in the future is a predictive process and uses historical AQI values to train and build models. In this work we focus on gap filling in air quality data where the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario is where one or a number of air, weather and traffic sensors are offline and explores prediction accuracy under such situations. The work is part of the MediaEval'2022 Urban Air: Urban Life and Air Pollution task submitted by the DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI, weather and CCTV traffic images for air pollution prediction.
