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研究生:許維中
研究生(外文):Hsu, Wei-Chung
論文名稱:車流量及發電對空汙的影響力分析
論文名稱(外文):Analysis of the influence of traffic flow and power generation on air pollution
指導教授:黃仕斌黃仕斌引用關係
指導教授(外文):Huang, Shih-Ping
口試委員:李昕潔周桂蘭
口試委員(外文):Li, Xin-jieChou, Kuei-Lan
口試日期:2020-06-19
學位類別:碩士
校院名稱:國立交通大學
系所名稱:科技管理研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:40
中文關鍵詞:空氣汙染機器學習敏感度分析車流量及發電對空汙的影響力
外文關鍵詞:Air pollutionMachine learningSensitivity AnalysisThe influence of traffic flow and power generation on air pollution
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空氣污染對健康產生的不良影響已經引起多數人的關注,空污形成的原因很多,擴散的範圍也很廣泛,除了季風所引起的境外汙染,境內汙染也是影響的因素之一,而在境內汙染中交通影響尤為重要,此外發電及氣象也是影響的要素。對於空汙的模型分析,若只著墨於大氣模型,對於政策上的措施較為被動,且要素的選擇不夠全面,本研究主要的研究目的是透過大數據的機器學習方法,將發電和交通一起與氣象納入其中,從而探討發電量與交通量對空污PM2.5影響因子的敏感度,藉由分析逐步減少各汙染源以達到較理想PM2.5數值的環境條件。研究結果先透過逐步廻歸的方式篩選較為重要的條件,使用剩下來的參數建立模型後,將交通、發電各自的條件因素透過敏感度分析,所欲分析的PM2.5數值以環境條件逐步10%百分比遞減,從0%至100%所有預測組合的PM2.5數值挑選最低的前110組繪製成條件熱圖,清楚了解若想降低PM2.5數值最有成效,需往哪些變數調整並且調整範圍大概坐落何處,最大化經濟效率的同時也兼顧環境的保護。在此研究的基礎上,能繼續發展使用有關時間因素的LSTM深度學習模型,預測未來參數的數值,再結合本研究模型與手法,以未來參數變化程度對未來的空汙數值影響,能預先防範類似空汙指數紫爆的風險。

關鍵字:空氣汙染、機器學習、敏感度分析、車流量及發電對空汙的影響力
The adverse effects of air pollution on health have attracted the attention of most people. There are many reasons for air pollution and scope of diffusion is also very wide. In addition to overseas pollution caused by the monsoon, domestic pollution is also one of the factors affecting. The traffic impact is particularly important in domestic pollution. Besides, power generation and meteorology are also important reasons for the impact. For the model analysis of air pollution, if we only focus on the atmospheric model, we will be more passive with regard to policy measures and the selection of elements is not comprehensive enough. The main research purpose of this research is to use machine learning methods to integrate power generation and traffic together with weather, so as to explore the sensitivity of power generation and traffic to the impact factors of air pollution PM2.5. Analyze the environmental conditions that gradually reduce the pollution sources to achieve a more ideal PM2.5 value. The results of the research are first used stepwise method for choose the more important conditions, then use the remaining parameters to build the model. Analyze the respective condition factors of traffic and power generation through sensitivity analysis, and the PM2.5 value to be analyzed will gradually decrease by 10% in terms of environmental conditions. From 0% to 100% of the PM2.5 values of all predicted combinations, select the lowest top 110 sets and draw a conditional heat map. Understand clearly what variables need to be adjusted if you want to reduce the value of PM2.5 to be the most effective, and where the adjustment range is approximately located, to maximize economic efficiency while also taking into account environmental protection. On the basis of this research, we can continue to develop the LSTM deep learning model that uses relevant time factors to predict the value of future parameters. Combined with this research model and method, The impact of future parameter changes on the future air pollution value can prevent the risk of similar air pollution index purple explosion in advance.

Keyword:Air pollution、Machine learning、Sensitivity Analysis、The influence of traffic flow and power generation on air pollution
目錄
摘 要 I
ABSTRACT II
誌 謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 研究流程 3
第二章文獻探討 4
2.1 機器學習應用於空氣汙染分析 4
2.2 演算法介紹 4
2.2.1 MLR(Multiple Linear Regression) 4
2.2.2 SVM(Support Vector Machine)/SVR(Support Vector Regression) 5
2.2.3 FNN(Feedforward Neural Network) 5
2.2.4 Regression Trees 6
2.2.5 Random Forest 6
2.2.6 KNN(k nearest neighbor) Regression 7
2.2.7 XGBoost(Extreme Gradient Boosting) 7
2.2.8 LSTM(Long short-term memory) 8
第三章研究方法 9
3.1 研究對象 9
3.2 研究架構 9
3.3 機器學習方法 10
3.3.1 多變量線性迴歸(Multiple Linear Regression, MLR) 10
3.3.2 支援向量迴歸(SVR, Support Vector Regression) 10
3.3.3 倒傳遞類神經網路(Backpropagation neural network, BPN) 14
3.3.4 Regression Trees 16
3.3.5 Random Forest 18
3.3.6 KNN Regression(k nearest neighbor regression) 19
3.3.7 XGBoost(Extreme Gradient Boosting) 19
3.3.8 LSTM(Long Short-Term Memory) 20
3.3.9 小結 23
3.4 檢定評估指標 30
3.4.1 平均絕對誤差(MAE, Mean Absolute Error) 30
3.4.2 均方根誤差(RMSE, Root Mean Square Error) 30
3.4.3 平均絶對百分比誤差(MAPE, Mean Absolute Percentage Error) 30
3.5 敏感度分析 31
第四章 研究內容 33
4.1 資料處理 33
4.2 機器學習模型選擇 34
4.3 影響因子強度敏感度分析 35
第五章 結論與建議 37
參考文獻 38
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