Description
The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. The proposed system uses machine learning- to accurately predict the Air Quality, using environmental monitoring data together with meteorological measurements. The comparisons with machine learning and deep learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.
Reviews
There are no reviews yet.