【摘要】 针对当下空气质量指数预测的模型精度不高的问题，提出一种基于蜣螂优化(DBO)算法，优化最小二乘支持向量机(LSSVM)的空气质量指数预测模型。该模型利用蜣螂优化算法对最小二乘支持向量机的两项参数进行寻优，提高预测速度和精度。并与传统最小二乘支持向量机、灰狼优化最小二乘支持向量机模型进行比对，通过实验仿真结果表明，蜣螂优化算法优化最小二乘支持向量机预测模型的均方误差、平均绝对误差及决定系数均为最优值，可以为空气质量指数预测提供更准确的支持。更多还原

【Abstract】 Aiming at the problem of low accuracy of the contemporary models for air quality index prediction, an air quality index prediction model based on dung beetle optimizer(DBO) algorithm optimized least squares support vector machine(LSSVM) is proposed. The model uses a dung beetle optimizer algorithm to find the best of two parameters of the least squares support vector machine to improve prediction speed and accuracy. And compare with the traditional least squares support vector machine and gray wolf optimization algorithm to optimize the least squares support vector machine model. The experimental simulation results show that the mean square error, mean absolute error and coefficient of determination of the least squares support vector machine prediction model optimized by the dung beetle optimization algorithm are optimal values, which can provide more accurate support for air quality index prediction.更多还原