The PM${}_{2.5}$ concentration model is the key to predict PM${}_{2.5}$ concentration. During the prediction of atmospheric PM${}_{2.5}$ concentration based on prediction model, the prediction model of PM${}_{2.5}$ concentration cannot be usually accurately described. For the PM${}_{2.5}$ concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM${}_{2.5}$ concentration may be different, and the single model cannot play the corresponding ability to predict PM${}_{2.5}$ concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM${}_{2.5}$ concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM${}_{2.5}$ concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM${}_{2.5}$ concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM${}_{2.5}$ concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM${}_{2.5}$ concentration prediction is improved in whole time period.

中文翻译：

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基于多模型自适应无味卡尔曼滤波的大气PM2.5预测

下午${}_{2.5}$ 浓度模型是预测PM的关键${}_{2.5}$专注。在大气PM预测中${}_{2.5}$ 基于预测模型的浓度，PM的预测模型${}_{2.5}$通常无法准确描述浓度。对于下午${}_{2.5}$在同一时期的浓度模型中，该模型的动态特性将在许多因素的影响下发生变化。同样，对于不同的时间段，相应的PM模型${}_{2.5}$ 浓度可能有所不同，并且单个模型无法发挥相应的预测PM的能力${}_{2.5}$专注。单一模型导致预测准确性下降。提高PM的精度${}_{2.5}$在该解决方案中的浓度预测中，提出了一种多模型自适应无味卡尔曼滤波（MMAUKF）方法。首先，PM${}_{2.5}$以一天中三个时间段的浓度数据为研究对象，建立了支持向量回归（SVR）方法的非线性状态空间模型框架。其次，将三个时间段内的SVR模型的帧与自适应无味卡尔曼滤波器（AUKF）组合以预测PM${}_{2.5}$接下来一个小时的浓度。然后，将三个时间段的预测值融合到最终的预测PM中${}_{2.5}$贝叶斯加权法计算浓度。最后，将所提出的方法与单支持向量回归自适应无味卡尔曼滤波器（SVR-AUKF），自回归模型-卡尔曼（AR-Kalman），自回归模型（AR）和反向传播神经网络（BP）进行了比较。预测结果表明PM的精度${}_{2.5}$ 在整个时间段内改善浓度预测。