Recurrent Learning on PM2.5 Prediction Based on Clustered Airbox Dataset

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81156

Title:	Recurrent Learning on PM2.5 Prediction Based on Clustered Airbox Dataset
Authors:	駱佳妤;Lo, Chia-Yu
Contributors:	資訊工程學系
Keywords:	空氣品質預測;分群;遞迴歸神經網路;Air quality prediction;clustering;recurrent neural network
Date:	2019-07-25
Issue Date:	2019-09-03 15:37:25 (UTC+8)
Publisher:	國立中央大學
Abstract:	工業發展的進步提升了對電力的需求。然而，核力發電的安全疑慮令人擔心，許多國家仍然依賴火力發電廠，這將導致在燃煤過程中產生更多的空氣污染物。這種現象的發生以及車輛排放量的增加，已經成為空氣污染嚴重的主要因素。當人類吸入過多的空氣污染懸浮微粒可能導致呼吸道的疾病甚至死亡，其中PM2.5 尤為嚴重。透過預測空氣污染物的濃度，人們可以採取預防措施，以避免過度暴露於空氣污染物中。因此，準確的預測PM2.5 濃度變得更加重要。在本文中，我們提出了一個PM2.5 濃度的預測系統，該系統使用了EdiGreen Airbox 和台灣環保署的數據。採用Autoencoder 和線性插值法來處理缺失值的問題。除此之外，Spearman 的相關係數用於識別與PM2.5 最相關的特徵。我們實做了兩個不同預測模型（即，LSTM 與基於Kmeans 的LSTM）來預測每個Airbox 設備的PM2.5 值。為了評估模型的預測性能，計算特定一周內的每日平均誤差和每小時平均的準確度。實驗結果顯示，基於K-means 的LSTM 在所有方法中具有最佳的預測能力。因此，選擇基於K-means 的LSTM 的方法結合Linebot 提供即時的PM2.5 預測。;The progress of industrial development naturally leads to the demand of 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 more 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 PM2.5. By predicting the air pollutant concentration, people can take precautions to avoid overexposure in the air pollutants. Consequently, the accurate PM2.5 prediction becomes more important. In this thesis, we propose a PM2.5 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 coe cient is used to identify the most relevant features for PM2.5. Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM2.5 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 PM2.5 prediction through the Linebot.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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