Due to the randomness and uncertainty in the atmospheric environment, and accompanied by a variety of unknown noise. Accurate prediction of PM_2.5 concentration is very important for people to prevent injury effectively. In order to predict PM_2.5 concentration more accurately in this environment, a hybrid modelling method of support vector regression and adaptive unscented Kalman filter (SVR-AUKF) is proposed to predict atmospheric PM_2.5 concentration in the case of incorrect or unknown noise. Firstly, the PM_2.5 concentration prediction model was established by support vector regression. Secondly, the state space framework of the model is combined with the adaptive unscented Kalman filter method to estimate the uncertain PM_2.5 concentration state and noise through continuous updating when the model noise is incorrect or unknown. Finally, the proposed method is compared with SVR-UKF method, the simulation results show that the proposed method is more accurate and robust. The proposed method is compared with SVR-UKF, AR-Kalman, AR and BP methods. The simulation results show that the proposed method has higher prediction accuracy of PM_2.5 concentration.

由于大气环境的随机性和不确定性，并伴有各种未知噪声。准确预测PM _2.5的浓度对于人们有效地预防伤害非常重要。为了在这种环境下更准确地预测PM _2.5浓度，提出了一种支持向量回归和自适应无味卡尔曼滤波器（SVR-AUKF）的混合建模方法，用于在噪声不正确或未知的情况下预测大气PM _2.5浓度。首先，通过支持向量回归建立了PM _2.5浓度预测模型。其次，将模型的状态空间框架与自适应无味卡尔曼滤波方法相结合，以估计不确定的PM。_2.5当模型噪声不正确或未知时，通过连续更新来集中状态和噪声。最后，将该方法与SVR-UKF方法进行了比较，仿真结果表明该方法更加准确，鲁棒。将该方法与SVR-UKF，AR-Kalman，AR和BP方法进行了比较。仿真结果表明，该方法具有较高的PM _2.5浓度预测精度。