Ecosense: a revolution in urban air quality forecasting for smart cities

Abstract

The Smart City (SC) framework is popular due to its advancement in enhancing lives and public safety. However, these advancements lead to many challenges due to the dependency of Internet of Things (IoT) devices in terms of electronic waste and resource consumption. To address those challenges, the integration of a weather-smart grid (WSG) with SC becomes crucial to safeguard the environment and residents’ well-being. Along with these concepts, this study proposes a novel approach, EcoSense: A Revolution in Urban Air Quality Forecasting for Smart Cities, which incorporates Bi-directional Stacked LSTM with a Weather-Smart Grid (BlaSt). BlaSt innovatively integrates several key components: (i) the model captures intricate temporal dependencies and trends in air quality data by incorporating historical air pollutant and meteorological data. (ii) integration of the WSG component enhances the model’s capability to incorporate weather data, which is critical for accurate air quality forecasting. (iii) the model computes 12-hour predictions by designing 1-hour prediction models, enabling it to provide timely forecasts with high precision. BlaSt demonstrates significant improvements over existing models, with enhancements of 36%, 26%, 21%, 46%, 14%, 10%, and 6% in accuracy compared to SVR, MLP, RAQP, Vlachogianni, LSTM, BLSTM, and SLSTM models, respectively. It achieves a mean absolute error (MAE) of 0.10 and a mean squared error (MSE) of 0.08. Additionally, BlaSt reduces computational complexity by 25%, making it more efficient in processing large-scale air quality data. The experimental results demonstrate BlaSt’s superior accuracy and efficiency, showcasing its potential to advance urban air quality forecasting in SCs.