Design and development of political rider competitive swarm optimiser enabled deep learning model for air quality detection
by Deepika Dadasaheb Patil; T.C. Thanuja; Bhuvaneshwari C. Melinamath
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 22, No. 1, 2024

Abstract: The air quality prediction process is a more significant one for air pollution prevention and management because air pollution becomes crueller. The precise identification of air quality has become a more significant concern for controlling air pollution. Recently, the weight of particulate matter (PM) on the human physical condition has become an important research area. In this paper, the political rider competitive swarm optimiser (PRCSO)-based deep recurrent neural network (DRNN) algorithm is devised for air quality and carbon monoxide prediction. The missing value imputation scheme is employed to perform pre-processing. Moreover, technical indicators and location information are extracted for the prediction process. The DRNN is employed for prediction, which is trained by the PRCSO and the training process is performed based on every location independently. The PRCSO-based DRNN outperforms existing techniques in terms of mean square error (MSE) of 0.0313, and mean absolute percentage error (MAPE) of 3.08%.

Online publication date: Mon, 05-Feb-2024

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