Metaheuristic Deep Learning Integrated with Evolutionary Optimization for Air Pollution Forecasting
DOI:
https://doi.org/10.32628/CSEIT26122Keywords:
PM2.5, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Deep Learning, Pollution Prevention, SustainabilityAbstract
Human activity, urbanization, and industrialization have all contributed to the gradual increase in air pollution that has been seen in a number of nations over the course of the last several decades. techniques of Deep Learning (DL) and Machine Learning (ML) have been of great assistance in the development of techniques in a variety of sectors, including the prediction, planning, and analysis of uncertainty in smart cities and urban progress in the present state of affairs. The rapid growth of both the population and the business sector has caused many big cities to have significant problems over the quality of the air (AQ). The most common pollutants are particulate matter (PM2.5) and particulate matter (PM10), and if the levels of these pollutants in the air continue to grow, they will be a threat to the health of people. In order to enhance the accuracy of air quality forecasts, a multitude of techniques that make use of deep learning approaches have been developed. These techniques include Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. According to the findings of this study, a combined Encoder STM model might be used to predict PM2.5. In addition to that, we proposed five more criteria that would make the forecasts more accurate. After that, further models, such as the LSTM model and the Bidirectional LSTM model, are examined for their ability to forecast the concentration of PM2.5. Our findings indicate that the suggested methodologies are superior to the advanced deep learning techniques that are currently in use in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). This accomplishment can be attributed to the fact that these methodologies have low error rates and limited feature sets.
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