基于时空相似LSTM的空气质量预测模型

由传统机器学习方法组成的空气质量预测模型得到了普遍应用,但是此类模型对于数据有效性,特别是时空相关数据的选取仍旧存在不足.针对深度学习输入数据有效性问题进行研究,提出了一种基于时空相似LSTM的预测模型(spatial-temporal similarity LSTM model,STS-LSTM),以便在时间和空间层面选取更加有效的数据.STS-LSTM分为前序、中序和后序三个模块,前序模块为时空相似选择输入模块,提出了格兰杰因果权重动态时间折叠(Granger causal index weighted dynamic time warping,GCWDTW)算法,用于选取具有更高时空相似性的数据;中序模块使用LSTM作为深度学习网络进行训练;后序模块根据目标站点特征选择不同的输出组合进行集成.STS-LSTM整体模型在空气质量预测误差上较现有算法提升了8％左右,经过有效性选取的数据对于模型精度达到了最高21％的提升.实验结果表明,对于有效数据的选取该算法取得了显著效果,将数据输入输出方法作为应用型深度学习网络的一部分,可以有效提升深度学习网络的最终效果.

Air quality prediction model based on spatial-temporal similarity LSTM

People have widely used air quality prediction models composed of traditional machine learning methods. However, such models still have shortage on data validity, especially the validity of spatial-temporal related data selection. In response to the input data validity of deep learning networks, this paper proposed a prediction model based on spatial-temporal similarity LSTM to select more effective data at the time and space level. STS-LSTM contained three modules, pre-order, middle-order and post-order module. The pre-order module was the time-space similar selection input module, which proposed the GCWDTW algorithm. This paper used it to select data with higher temporal and spatial similarity. The in-order module used LSTM as a deep learning network for training. The post-order module selected different output groups for integration according to the characteristics of the target station. The STS-LSTM overall model improves the air quality prediction error by about 8% compared with the existing algorithm, and the data selected by the validity improves the accuracy of the model by up to 21%. The experimental results show that this model has achieved significant results in the effective data selection, and the data input and output method as a part of the applied deep learning network can effectively improve the final effect of the deep learning network.