Air pollution forecast has become critical because of its direct impact on human health and the increased production of air pollutants caused by rapid industrialization. Machine learning (ML) solutions are being drastically explored in this domain because of their potential to produce highly accurate results with access to historical data. However, experts in the environmental area are skeptical about adopting ML solutions in real-world applications and policy-making due to their black-box nature. In contrast, despite having low accuracy sometimes, the existing traditional simulation models (e.g., CMAQ) are widely used and follow well-defined and transparent equations. Therefore, presenting the knowledge learned by the ML model can make it transparent as well as comprehensible. In addit...[ Read more ]

Air pollution forecast has become critical because of its direct impact on human health and the increased production of air pollutants caused by rapid industrialization. Machine learning (ML) solutions are being drastically explored in this domain because of their potential to produce highly accurate results with access to historical data. However, experts in the environmental area are skeptical about adopting ML solutions in real-world applications and policy-making due to their black-box nature. In contrast, despite having low accuracy sometimes, the existing traditional simulation models (e.g., CMAQ) are widely used and follow well-defined and transparent equations. Therefore, presenting the knowledge learned by the ML model can make it transparent as well as comprehensible. In addition, validating the ML model’s learning with the existing domain knowledge might aid in addressing the expert’s skepticism, building appropriate trust, and better utilizing ML models. In collaboration with three experts having an average of five years' research experience in the air pollution domain, we identified that feature (meteorological feature like wind) contribution towards the final forecast as the vital information to be verified with domain knowledge. In addition, the performance of the ML model compared with the traditional simulation model and visualization of raw wind trajectories are essential for domain experts to validate the feature contribution information. We designed and developed AQX, a visual analytics system to help experts validate and verify the ML model’s learning with their domain knowledge based on the identified information. The system includes coordinated multiple views to present the contributions of input features at different levels of aggregation in both temporal and spatial dimensions. It also provides a performance comparison of ML and traditional models in terms of accuracy and spatial map, along with the animation of raw wind trajectories for the input period. We further demonstrated two case studies and conducted expert interviews with two domain experts to show the effectiveness and usefulness of AQX.