一种卷积自编码深度学习的空气污染多站点联合预测模型

A Multi-Site Joint Air Pollution Prediction Model Based on Convolutional Auto-Encoder Deep Learning

城市空气污染因空间扩散特性呈现出区域内的浓度高关联性.因此如何通过多个空气污染监测站的时空数据预测特定目标地点的污染情况,以解决站点分布不匀的问题,是一个重要的研究工作.本文结合空气污染物因素特性和气象因素的多维度影响,提出了一个利用区域内多站点空间监测数据实现特定目标站点的空气污染物浓度预测模型.该模型通过多层卷积神经网络(Convolutional Neural Network,CNN)实现城市多站点污染物浓度与气象数据之间的维度关联特征及空间关联特征学习,进而利用基于多层长短期记忆网络(Long Short-Term Memory,LSTM)的自编码网络实现多站点浓度的时序关联特征分析.实验通过真实数据集验证,所提出的预测模型获得了高于传统机器学习污染物浓度预测模型的预测准确度,且在多个城市数据集上验证了模型的泛化能力.

Due to the nature of spatial diffusion,urban air pollution presents a high correlation with regional concentration feature.Therefore,how to use spatio-temporal related data from urban multiple air pollution detection sites to predict air pollution concentration of a special target location is an important research effort for solving the problem of uneven site distribution.Concerning the multi-dimensional impacts of the air pollutant factors’features and the influence of meteorological factors,we propose an air pollutant concentration prediction model,which uses multi-site spatial detection data within a region to predict the concentration of the target station in this paper.This model is able to learn dimensional correlation characteristics and spatial correlation characteristics from multi-site pollutant concentration and meteorological data in the urban area through the multi-layer convolutional neural network,and then analyze the time-series correlation characteristics of multi-site concentration by utilizing the multi-layer auto-encoder network based long short-term memory network.The experimental results show that our proposed model obtains better performances than traditional machine learning based models under a real-world dataset,and meanwhile,the generalization performance of the proposed model has been examined based on multiple urban air pollution database.