Application of Improved Beluga Whale Optimization convolutional network for air quality prediction in Yinchuan City
The Air Quality Index(AQI)is a vital indicator for assessing and monitoring air pollution levels,offering scientific evidence for environmental protection.Accurate prediction of the AQI is essential for government efforts in implementing targeted pollution control measures and regulations,thereby enhancing urban air quality.Therefore,this paper proposes an air quality index prediction model that combines Factor Analysis(FA)with an Improved Beluga Whale Optimization(IBWO)algorithm to optimize a Convolutional Neural Network(CNN).The model utilizes historical air quality monitoring data from Yinchuan City,spanning from October 28,2013,to May 31,2023,to validate its effectiveness.First,Factor Analysis is employed to examine six pollution indicators in the dataset:PM2.5,PM10,CO,NO2,SO2,and O3.The factor loading matrix is then used to calculate the score of each pollution indicator for each new factor,ultimately resulting in a novel factor representation scheme for air pollution indicators.Subsequently,a CNN-based AQI prediction model is developed for forecasting the AQI in Yinchuan City.Additionally,this paper introduces a dynamic threshold strategy and a Beluga Whale sickness strategy to enhance the original Beluga Whale Optimization(BWO)algorithm.The improved IBWO is employed to optimize two hyperparameters of the CNN model:the number of neurons in the fully connected layer and the learning rate.This optimization aims to identify the optimal hyperparameters that achieve the best prediction performance.Finally,the proposed model is compared with other existing models.The research results indicate that,compared to the ELM model,SVR model,and CNN models optimized by other intelligent optimization algorithms,the proposed model demonstrates superior performance across all metrics,including average absolute error(NMAE),normalized root mean square error(NRMSE),goodness of fit(R2),and normalized mean absolute percentage error(NMAPE).Therefore,the model proposed in this paper demonstrates strong predictive performance and wide applicability.It offers accurate predictions in practical applications,enabling individuals to make informed decisions and effective plans.
万方数据
维普
