Recurrent Learning on PM2.5 Prediction Based on Clustered Airbox Dataset
Recurrent Learning on PM2.5 Prediction Based on Clustered Airbox Dataset
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DOI：
10.1109/tkde.2020.3047634
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发表时间：
2020-12
影响因子：
8.9
通讯作者：
Chia-yu Lo;Wenxi Huang;Ming-Feng Ho;Min-Te Sun;Ling-Jyh Chen;Kazuya Sakai;Wei-Shinn Ku
中科院分区：
文献类型：
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作者：
Chia-yu Lo;Wenxi Huang;Ming-Feng Ho;Min-Te Sun;Ling-Jyh Chen;Kazuya Sakai;Wei-Shinn Ku
工业发展的进步自然导致对更多电力的需求。不幸的是，由于担心核电站的安全性，许多国家都依赖火力发电厂，这将在燃煤过程中造成更多的空气污染物。这种现象，加上我们周围的汽车废气增加，构成了严重空气污染的主要因素。吸入过多的颗粒空气污染可能导致呼吸道疾病甚至死亡，特别是PM$_{2.5}$2.5。通过预测空气污染物浓度，人们可以采取预防措施，以避免过度暴露于空气污染物。因此，准确的PM$_{2.5}$2.5预测变得更加重要。在这项研究中，我们提出了一个PM$_{2.5}$2.5预测系统，它利用了EdiGreen Airbox和台湾EPA的数据集。采用自动编码器和线性插值法解决了数据丢失问题。斯皮尔曼相关系数用于识别PM$_{2.5}$2.5的最相关特征。两个预测模型（即，LSTM和基于K-means的LSTM）被实现，其预测每个Airbox设备的PM$_{2.5}$2.5值。为了评估模型预测的性能，计算一周内的日平均误差和小时平均准确度。实验结果表明，基于K-means的LSTM在所有方法中具有最好的性能。因此，选择基于K-means的LSTM通过Linebot提供实时PM$_{2.5}$2.5预测。
The progress of industrial development naturally leads to the demand for more electrical power. Unfortunately, due to the fear of the safety of nuclear power plants, many countries have relied on thermal power plants, which will cause more air pollutants during the process of coal burning. This phenomenon as well as increased vehicle emissions around us, have constituted the primary factors of serious air pollution. Inhaling too much particulate air pollution may lead to respiratory diseases and even death, especially PM$_{2.5}$2.5. By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM$_{2.5}$2.5 prediction becomes more important. In this study, we propose a PM$_{2.5}$2.5 prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Autoencoder and Linear interpolation are adopted for solving the missing value problem. Spearman’s correlation coefficient is used to identify the most relevant features for PM$_{2.5}$2.5. Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM$_{2.5}$2.5 value for each Airbox device. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K-means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time PM$_{2.5}$2.5 prediction through the Linebot.