TL;DR
The results demonstrate that the proposed aggregated deep learning framework consistently outperforms existing methods across both benchmark datasets while maintaining high computational efficiency.
Air pollution prediction is a key research problem due to its direct impact on public health and the urban environment. Moreover, because air pollutants are dynamic and nonlinear, this poses a complex challenge for time-series modeling. However, while there has been significant development in the use of deep learning–b...
A Temporal-Aware Multi-Task Learning (TMTL-AQI) framework to assess urban air quality via structured data that outperforms single-task and baseline multi-task models with an accuracy of 0.7605 and an F1-score of 0.7422.
Experimental results reveal that the DL methods achieve significantly better forecasting of temperature as compared to the conventional ARIMA model, providing strong evidence of the value of combining DL methods for weather prediction.
Pollution of air is one of the most critical environmental and public-health problems in urban regions and it is important to have predictive models to predict and give early warning. The literature survey of the paper covers all the machine learning and deep learning models used in air quality index (AQI) and concentr...
A replicable, data-driven blueprint for intelligent air quality governance in the era of connected urban environments is established, establishing a replicable, data-driven blueprint for intelligent air quality governance in the era of connected urban environments.
The increasing rate of urbanization, industrialization, and car emissions, air quality has emerged as an important environmental and health issue. The issues concerning early warning systems, environmental policies, and environmental and health hazards can be diminished by precise air quality monitoring and prediction....
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