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研究生(外文):Fan, Chien-Hung
論文名稱(外文):Analysis of PM2.5 Prediction Based on Gas Emission Data and Machine Learning
指導教授(外文):Tai, Li-Chia
口試委員(外文):Lin, Shean-YihHuang, Sheng-Chieh
外文關鍵詞:PM2.5 forecastingMachine learningTime series dataLSTMCNN-LSTM
  • 被引用被引用:0
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With the advancement of technology and the growth of population, the destruction of the environment by human beings increase rapidly. Air pollution has become a prevalent issue, and PM2.5, as one of the evaluation indicators of air pollution, will cause discomfort and respiratory diseases to the human. In order to effectively prevent PM2.5, in addition to finding ways to reduce pollution, how to accurately forecast future environmental changes and prevent air pollution in advance is also one of the important topics. Therefore, this research focuses on improving the PM2.5 forecasting method by the data science and artificial intelligence.
We use the air quality information in the past to predict the future PM2.5 concentration changes, and compare the model of different algorithms such as CNN-LSTM, LSTM, XGBoost, SVR, and KNN Regression. Based on correlation analysis, the influence of different air quality features on model performance is also compared. Furthermore, we propose an adaptive seasonal prediction method and the framework of the classification and regression algorithms to make forecasting by using the similarity to distinguish the data and train individually, which improving the model to adapt to specific data and make more accurate predictions. Finally, a prediction model that adopts the change of the single variable of PM2.5 is proposed to establish a fast prediction, which saves training time and reduces the demand for sensing devices.
摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 viii
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究貢獻 2
1.4 論文架構 3
第二章、相關研究 4
2.1 懸浮微粒相關介紹 4
2.2 演算法介紹 5
2.3 文獻討論 20
第三章、研究方法 22
3.1 開發環境 22
3.2 研究流程 23
3.3 資料處理 24
3.4 預測模型及評估指標 26
第四章、研究結果 28
4.1 特徵選取 28
4.2 演算法比較 31
4.3 PM2.5週期性分析 36
4.4 K-means 聚類分析 40
4.5 PM2.5單變量預測方法 43
第五章、結論與未來展望 45
5.1 結論 45
5.2 未來展望 46
參考文獻 47
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