基于BP-SVR组合模型的空气质量指数预测

建立有效的空气质量指数预测模型，可以为个人出行及相关部门治理大气污染提供指导。选取北京市的历史空气数据以及气象数据作为研究对象，建立基于BP（Back Propagation）神经网络和SVR（Support Vector Regression）支持向量机回归的BP-SVR组合预测模型。首先利用灰狼优化算法分别对BP模型和SVR模型参数进行寻优；然后运用该组合模型对空气质量指数进行预测。实验结果表明，BP-SVR模型的平均绝对百分误差、均方根误差、平均绝对误差均小于单一预测模型，分别为0.217 5、37.032 0、25.157 5。BP-SVR组合模型具有更高的预测精度，泛化能力更强，可以对空气质量指数进行有效预测。

Air Quality Index Prediction Based on BP-SVR Combination Model

Establishing an effective air quality index prediction model can provide guidance for individual travel and related departments to control air pollution. The historical air data and meteorological data of Beijing were selected as research objects， and a BP-SVR combined prediction model based on BP （Back Propagation） neural network and SVR （Support Vector Regression） support vector machine regression was established. First， the gray wolf optimization algorithm was used to optimize the parameters of the BP model and the SVR model， and then the combined model was used to predict the air quality index. Experimental results show that the average absolute percentage error， root mean square error， and average absolute error of the BP-SVR model are smaller than that of a single prediction model， which are 0.2175， 37.032 0， and 25.157 5， respectively. The BP-SVR combination model has higher prediction accuracy and stronger generalization ability， and can effectively predict the air quality index.