資料載入中.....
|
請使用永久網址來引用或連結此文件:
https://irlib.pccu.edu.tw/handle/987654321/48861
|
| 題名: | Forecasting Air Quality in Taiwan by Using Machine Learning |
| 作者: | Lee, M (Lee, Mike)
Lin, L (Lin, Larry)
Chen, CY (Chen, Chih-Yuan)
Tsao, Y (Tsao, Yu)
Yao, TH (Yao, Ting-Hsuan)
Fei, MH (Fei, Min-Han)
Fang, SH (Fang, Shih-Hau) |
| 貢獻者: | 地理系 |
| 關鍵詞: | PM2.5 CONCENTRATIONS |
| 日期: | 2020-12-01 |
| 上傳時間: | 2020-12-10 13:53:49 (UTC+8) |
| 摘要: | This study proposes a gradient-boosting-based machine learning approach for predicting the PM2.5 concentration in Taiwan. The proposed mechanism is evaluated on a large-scale database built by the Environmental Protection Administration, and Central Weather Bureau, Taiwan, which includes data from 77 air monitoring stations and 580 weather stations performing hourly measurements over 1 year. By learning from past records of PM2.5 and neighboring weather stations' climatic information, the forecasting model works well for 24-h prediction at most air stations. This study also investigates the geographical and meteorological divergence for the forecasting results of seven regional monitoring areas. We also compare the prediction performance between Taiwan, Taipei, and London; analyze the impact of industrial pollution; and propose an enhanced version of the prediction model to improve the prediction accuracy. The results indicate that Taipei and London have similar prediction results because these two cities have similar topography (basin) and are financial centers without domestic pollution sources. The results also suggest that after considering industrial impacts by incorporating additional features from the Taichung and Thong-Siau power plants, the proposed method achieves significant improvement in the coefficient of determination (R-2) from 0.58 to 0.71. Moreover, for Taichung City the root-mean-square error decreases from 8.56 for the conventional approach to 7.06 for the proposed method. |
| 關聯: | SCIENTIFIC REPORTS 卷冊: 10 期: 1 文獻號碼: 4153 |
| 顯示於類別: | [地理學系] 期刊論文
|
文件中的檔案:
| 檔案 |
描述 |
大小 | 格式 | 瀏覽次數 |
|---|
| index.html | | 0Kb | HTML | 391 | 檢視/開啟 |
|
在CCUR中所有的資料項目都受到原著作權保護.
|
