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A PM2.5 concentration prediction model based on multi-task deep learning for intensive air quality monitoring stations

机译：基于多任务深度学习对密集空气质量监测站的PM2.5浓度预测模型

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With the deployment and real-time monitoring of a large number of micro air quality monitoring stations, new application scenarios have been provided for the research of air quality prediction methods based on artificial intelligence. Integrating deep learning with multi-task learning, this paper proposes a hybrid model for air quality prediction to leverage data from intensive air quality monitoring stations. The proposed model consists of a shared layer, a task-specific layer, and a multi-loss joint optimization module. It is tested on three monitoring stations located in three different districts of Lanzhou City, China, for PM2.5 concentration prediction. The results show that: (1) When the number of convolutional layers of convolutional neural network in the shared layer and the number of gated recurrent unit layers in the task-specific layer exist in two layers, model performs the best, and its predictability of the optimization algorithm with early-stopping will be significantly improved. (2) Using the proposed model to predict PM2.5 concentration on horizon t + 1, the mean absolute error and root mean square error are 4.54 and 7.96, respectively, indicating better performance in intensive air quality prediction than previous models based on simple hybridization. (3) The predictive performance on different stations is different, and the proposed model performs better than other models when there are large fluctuations and sudden changes in the data. Overall, the proposed model has good temporal stability and generalization ability and provides a new method for air quality prediction in intensive air quality monitoring scenarios. (C) 2020 Elsevier Ltd. All rights reserved.
机译：随着大量微空气质量监测站的部署和实时监测，已经为基于人工智能的空气质量预测方法进行了新的应用方案。通过多任务学习整合深度学习，本文提出了一种用于空气质量预测的混合模型，从而利用来自密集的空气质量监测站的数据。所提出的模型包括共享层，特定于任务层和多丢失联合优化模块。它对位于中国兰州市三个不同地区的三个监测站进行了测试，用于PM2.5浓度预测。结果表明：（1）当共享层中的卷积神经网络的卷积层数的数量和任务特定层中的门控复发单元层的数量存在于两层中时，模型表现了最佳，其可预测性早期停止的优化算法将显着提高。 （2）使用所提出的模型来预测地平线T + 1上的PM2.5浓度，平均绝对误差和根均方误差分别为4.54和7.96，表明基于简单杂交的先前模型表现出更好的性能。 。 （3）不同站的预测性能是不同的，并且当存在大的波动和数据突然变化时，所提出的模型比其他模型更好。总的来说，该模型具有良好的时间稳定性和泛化能力，并为密集的空气质量监测方案提供了一种新的空气质量预测方法。 （c）2020 elestvier有限公司保留所有权利。

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