基于预测数据特征的空气质量预测方法

采用LightGBM预测模型对空气质量预测问题进行研究,提出并设计一种基于预测性特征的空气质量预测方法,有效地预测北京市区内未来24 h核心表征空气质量的PM_2.5质量浓度。在构建预测方案过程中,分析训练数据集特性开展数据清洗,利用随机森林与线性插值相结合的方法,解决数据大量缺失以及噪声干扰问题;提出使用预测性数据特征方法,同时设计相关统计特征,提高预测结果的准确性;采用滑窗机制挖掘高维时间特征,增加数据特征数量级;对预测模型的工作性能和结果进行详细分析,并结合基线模型进行对比评价。试验结果表明,基于预测性特征结合采用LightGBM预测模型的方案具有更高的预测精度。

Air quality prediction approach based on integrating forecasting dataset

Towarding the air quality prediction research problem, LightGBM was employed to propose and design a predictive feature-based air quality prediction approach, which could effectively predict the PM_2.5 concentration, i.e., the key indicator reflecting air quality, in the upcoming 24-hour within Beijing. During constructing the prediction solution, the features of the training data set was analyzed to execute data cleansing, and the methods of random forest and linear interpolation were used to solve the problem of high data loss and noise interference. The predictive data features were integrated into the dataset, and meanwhile the corresponding statistical features were designed to imiprove the prediction accurancy. The sliding window mechanism was used to mine high-dimensional time features and increase the quantity of data features. The performance and result of the proposed approach were analyzed in details through comparing with the basedline models. The experimental results showed that compared with other model methods, the proposed LightGBM-based prediction approach with integrating forecasting data had higher prediction accuracy.