基於遞迴神經網路之深度學習建立PM2.5預測模型

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Please use this identifier to cite or link to this item: http://140.128.103.80:8080/handle/310901/31853

Title:	基於遞迴神經網路之深度學習建立PM2.5預測模型
Other Titles:	A PM2.5 Prediction Model Based on Deep Learning with Recurrent Neural Network
Authors:	莊宜庭 CHUANG, YI-TING
Contributors:	姜自強 CHIANG,TZU-CHIANG 資訊管理學系
Keywords:	長短期記憶模型;深度學習;相關係數;主成分分析;空氣污染;遞迴神經網路 LSTM;RNN;Deep learning;Correlation Analysis;Air pollution　　　　　;Principal Components Analysis
Date:	2019
Issue Date:	2019-12-16T06:53:48Z (UTC)
Abstract:	近年來由於許多的研究發表都驗證空污會嚴重影響到人體健康，再加上媒體報導許多有關空污的議題，因此更讓民眾開始重視它的存在。本研究以2018年環保署的空氣品質即時污染指標的資料來做分析，採用五種補值的方法進行補值，藉由主成分分析和相關係數各別找出影響PM2.5濃度的主要相關變數（單因子：PM10、SO2、NOX、NO2、CO，雙因子：NOX＋NO2＋CO、SO2＋PM10），並利用遞迴神經網路（RNN）的長短期記憶模型（LSTM）來建立預測未來8小時的PM2.5濃度模型。根據研究結果顯示，豐原測站的預測值與真實值之誤差大部分都有落在合理的MAPE（0.2~0.5）範圍內。另外在補值法方面是以線性插值法最好。 In recent years, many studies have verified that air pollution will seriously affect human health. In addition, the media reported many issues concerning air pollution, so people have begun to pay attention to its existence. This study analyzes the data of the Environmental Protection Administration air quality immediate pollution indicators in 2018. Five methods are used to deal with the missing values. The main correlation variables affecting the PM25 concentration are identified by principal component analysis and correlation coefficients (single factor: PM10, SO2, NOX, NO2, CO, two-factor: NOX+NO2+CO, SO2+PM10), and the Long-Short Term Memory Model (LSTM) of the Recurrent Neural Network (RNN) was used to model the PM25 concentration model for the next 8 hours. According to the research results, most of the errors between the predicted and true values of Fengyuan Station fall within the reasonable range of MAPE (0.2~0.5). In addition, the best way to deal with the missing value is linear interpolation.
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