Managing Large Dataset Gaps in Urban Air Quality Prediction: DCU-Insight-AQ at MediaEval 2022

TL;DR

This paper addresses the challenge of predicting future air quality index (AQI) values during sensor outages by developing models that utilize multimodal data, including AQI, weather, and traffic images, to fill data gaps.

Contribution

It introduces a novel approach for gap filling in air quality data using multimodal and crossmodal data sources for improved prediction accuracy during sensor outages.

Findings

Effective prediction of AQI 1, 5, and 7 days ahead during sensor outages.

Utilization of multimodal data improves gap filling accuracy.

Part of MediaEval 2022 Urban Air Pollution task.

Abstract

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.