Deep Learning-Based Air Pollution Prediction in Smart Cities

One of the most significant issues affecting urban living is air pollution, a form of environmental hazard. Particles having a diameter of under 2.5 µm (PM2.5) is one of the air contaminants that causes a variety of ailments. Therefore, to be able to shield populace using the potentially harmful effects of airborne toxins in advance, it is essential to precisely anticipate PM2.5 concentrations. It is now feasible to anticipate air quality in smart cities using deep learning techniques and their use in practically all facets of life. Using a deep learning solution, CNN Bi-LSTM, with a geographical time-based data, and data from the past on contaminant, weather, PM2.5 concentration at nearby stations, it is possible to predict the hourly prediction of air quality in India. Additionally, compared the performance differences across algorithms for deep learnings like LSTM, GRU, Bi-GRU, Bi-LSTM, CNN LSTM, and the proposed CNN-Bi-LSTM model. The experiment’s findings indicate that the mean absolute error of CNN Bi-LSTM and the RMSE both experience drops of 0.20224 and 0.33256, respectively. CNN Bi-LSTM gives the greatest results for air pollution prediction when compared to BI-LSTM and LSTM, and its training time is cut by 12.5 and 50.9 cent, respectively. The primary conclusions of this study based on performance assessment and result comparison: the proposed model can successfully capture the spatial and time aspects of the data using CNN and Bi-LSTM, with more precisely and has stability