基于LSTM与注意力建模的空气质量站模拟
Air Quality Station Simulation via LSTM and Attention-Based Modelling
- GATE Institute（GATE研究所）
- Sofia University “St Kliment Ohridski”（索非亚大学“圣克莱门特·奥赫里德斯基”）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出名为SATADL的深度学习模型，基于LSTM与注意力建模，可模拟故障空气质量站的PM10浓度，在四组全球数据集上的表现优于基线模型，适合作为虚拟代理站。
AI中文摘要：
城市地区的空气质量差由复杂的过程链驱动，是重大的公共卫生问题。为了更好地理解和控制决定空气质量的机制，城市部署了测量站网络，并启动了收集大气污染物浓度更密集数据的举措。从测量站提取见解依赖于它们可靠且不间断的运行，但硬件易受故障和停机影响，可能导致数据不可用，进而影响整体分析质量。本文提出了一种名为SATADL的深度学习模型，该模型可推断复杂关系并输出未来数小时的空气质量预测，目标是模拟无响应测量站的测量值，直至其恢复运行。模型架构可从数据的不同方面提取信息，本文详细描述了该架构并对其所有组件进行了仔细检查。我们在全球四组空气质量站数据集上验证了SATADL的性能，模拟其中一个测量站在长达48小时的假设故障期间的PM10浓度，并使用一系列基线和已发表的深度学习模型作为基准。结果表明，在不同预测窗口下，SATADL在决定系数和均方根误差两个指标上均表现更优，证明其适合作为虚拟代理站。
英文摘要：
Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern. To better understand and control the mechanisms that determine air quality, cities deploy networks of measurement stations, and launch initiatives for collecting denser data about the concentration of pollutants in the atmosphere. Extracting insights from the stations relies on their reliable and uninterrupted operation. However, hardware is susceptible to faults and black- outs that may result in data unavailability, which affects the overall quality of analyses. In this paper, we present a deep-learning model, called SATADL, which can infer complex relations and output multiple-hour-ahead air-quality forecasts. The goal of the model is to simulate the mea- surements of an unresponsive station until its operation is restored. The architecture of the model, which allows it to extract information from different aspects of the data, is described in detail and a careful examination of all of its components is provided. We demonstrate the performance of SATADL on four sets of air quality stations from around the world, by using it to simulate the concentration of PM10 for periods of hypothetical failures of one of the measurement stations, lasting for as long as 48 hours. A selection of baseline and published deep learning models were trained and used as a benchmark. The results show that SATADL per- forms better across different prediction windows, for both coefficient of determination and root mean squared error, demonstrating its suitability as a virtual proxy station.
