基于时空大数据的实时空气污染预测模型 (Real-time Air Pollution prediction model based on Spatiotemporal Big data)

from arxiv, - We fix typos and grammars through out the paper. - We fix layout of figure for better view. - We update mathematic formula and its description. - We add more insights to experimental results. - We correct author names in references

Air pollution is one of the most concerns for urban areas. Many countries have constructed monitoring stations to hourly collect pollution values. Recently, there is a research in Daegu city, Korea for real-time air quality monitoring via sensors installed on taxis running across the whole city. The collected data is huge (1-second interval) and in both Spatial and Temporal format. In this paper, based on this spatiotemporal Big data, we propose a real-time air pollution prediction model based on Convolutional Neural Network (CNN) algorithm for image-like Spatial distribution of air pollution. Regarding to Temporal information in the data, we introduce a combination of a Long Short-Term Memory (LSTM) unit for time series data and a Neural Network model for other air pollution impact factors such as weather conditions to build a hybrid prediction model. This model is simple in architecture but still brings good prediction ability.

翻译：空气污染是城市地区最为关注的问题之一。许多国家已建立监测站，每小时收集污染数据。近期，韩国大邱市开展了一项研究，通过在全市运行的出租车上安装传感器，实现实时空气质量监测。所收集的数据量巨大（间隔1秒），且同时包含空间与时间维度。本文基于此类时空大数据，提出了一种基于卷积神经网络（CNN）算法的实时空气污染预测模型，用于处理类似图像的空间分布污染数据。针对数据中的时间信息，我们引入了长短期记忆（LSTM）单元处理时间序列数据，并结合神经网络模型分析天气条件等其他污染影响因素，构建了一种混合预测模型。该模型结构简洁，但仍具备良好的预测能力。