臭氧事件之預測分析-以高雄左營地區為例

Prediction of Ozone Episode Days- A Case Study of Zuoying Area in Kaohsiung

李振豪(Chen-Hao Li)

指導教授 ： 黃富國

淡江大學/工學院/水資源及環境工程學系碩士班/碩士(2012年)

https://doi.org/10.6846/TKU.2012.00165

摘要

高屏地區之光化學污染問題值得長期關注，如何預測及降低臭氧濃度為目前空氣品質污染防制的重要課題。本研究以高雄市臭氧污染較嚴重之左營地區為例，分別採用判別分析(discriminant analysis, DA)、邏輯迴歸(logistic regression, LR)、類神經網路(artificial neural network, ANN)，以及較新之支持向量機(support vector machine, SVM)等四種模式作臭氧事件日之預測分析，各模式之預測成果雖互有差異，但相較於直接預測臭氧濃度，本研究展示了探討臭氧問題之另一可行途徑。
本文之研究，利用包括檢測機率(POD)、誤警率(FAR)、誤判率(FPR)、漏判率(FNR)及準確率(ACC)等指標作為模式優劣評判之依據。依美國臭氧事件標準，於臭氧事件日與否之資料數目有偏差的情況下，DA雖然有最高之POD，但卻有最差之FAR及FPR，導致預測結果表現不佳。而SVM之FAR、FPR及ACC雖然不錯，整體而言，還是以ANN呈現之預測結果最為理想；而依台灣臭氧事件標準，在臭氧事件日與否之資料數目沒有偏差之情況下，顯示DA及LR模式在預測臭氧事件日之表現上均較不理想。ANN有最高之準確率ACC、最低之FPR及最低之FAR。相較之下，SVM雖有較高之FAR，不過有最高之POD及最低之FNR；整體表現雖不如ANN理想，不過POD值、FNR值均表現不錯，若考量臭氧事件對民眾健康的影響，採用SVM模式來預測臭氧事件日，仍為一可資採用之選擇。

關鍵字

臭氧事件日 ； 判別分析 ； 邏輯迴歸 ； 類神經網路 ； 支持向量機

並列摘要

It’s a problem worthy of much attention in the long term for the photochemical air pollution in Kaohsiung/Ping-Dong area. How to predict and reduce the ozone concentration is an important issue for the air quality pollution control. In this research, a case study of ozone pollution is performed for the Zuoying area in Kaohsiung. In order to predict the ozone episode days, four models, i.e., discriminant analysis (DA), logistic regression (LR), artificial neural network (ANN), and the support vector machine (SVM), are used to analysis and comparison, respectively. Rather than predicting the ozone concentration directly, the occurrence of ozone episode days is forecast instead in the analysis which demonstrates another way to explore the ozone issue.
In this study, five indicators including probability of detection (POD), false alarm rate (FAR), false positive rate (FPR), false negative rate (FNR) and accuracy (ACC) are used as a basis to assess the model performance. If based on the biased data according to the U.S. standard for ozone episodes, DA has the highest POD, but with the worst FAR and FPR, led to the poor performance of the prediction results. Whereas SVM has the better FAR, FPR, and the ACC, the performance of ANN prediction is the best overall. On the other hand, if based on the unbiased data according to the Taiwan standard for ozone episodes, the performance of DA and LR models is not satisfactory. ANN has the highest ACC and the lowest FPR and FAR. In contrast, although SVM has the higher FAR, it with the highest POD and the lowest FNR. The performance of SVM is not good than ANN in general, however, its POD and FNR values show good. If the influences of ozone episodes on the public health are considered, it will be a not bad choice by the SVM model to predict the ozone episode days.

並列關鍵字

ozone episodes ； discriminant analysis ； logistic regression ； artifical neural network ； support vector machine

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