Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts

doi:10.1016/j.jclepro.2018.10.243

淡江大學機構典藏 > 工學院 > 水資源及環境工程學系暨研究所 > 期刊論文 > Item 987654321/116054

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/116054

题名:	Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts
作者:	Yanlai Zhou;Fi-John Chang;Li-Chiu Chang;I-Feng Kao;Yi-Shin Wang
关键词:	Multi-output LSTM;Deep learning;Artificial intelligence (AI);Multi-step-ahead forecast;Air quality;Taipei city
日期:	2019-02
上传时间:	2019-03-23 12:11:09 (UTC+8)
出版者:	Elsevier
摘要:	Timely regional air quality forecasting in a city is crucial and beneficial for supporting environmental management decisions as well as averting serious accidents caused by air pollution. Artificial Intelligence-based models have been widely used in air quality forecasting. The Shallow Multi-output Long Short-Term Memory (SM-LSTM) model is suitable for regional multi-step-ahead air quality forecasting, while it commonly encounters spatio-temporal instabilities and time-lag effects. To overcome these bottlenecks and overfitting issues, this study proposed a Deep Multi-output LSTM (DM-LSTM) neural network model that were incorporated with three deep learning algorithms (i.e., mini-batch gradient descent, dropout neuron and L2 regularization) to configure the model for extracting the key factors of complex spatio-temporal relations as well as reducing error accumulation and propagation in multi-step-ahead air quality forecasting. The proposed DM-LSTM model was evaluated by three time series of PM2.5, PM10, and NOx simultaneously at five air quality monitoring stations in Taipei City of Taiwan. Results indicated that the loss function values (mean-square-error) of the SM-LSTM and DM-LSTM models in the testing stages at horizon t+4 were 0.87 and 0.72, respectively. The Gbench values of the DM-LSTM model in the testing stages for PM2.5, PM10, and NOx reached 0.95 at horizon t+1 and exceeded 0.81 at horizon t+4, respectively. Results demonstrated that the proposed DM-LSTM model incorporated with three deep learning algorithms could significantly improve the spatio-temporal stability and accuracy of regional multi-step-ahead air quality forecasts.
關聯:	Journal of Cleaner Production 209, p.134-145
DOI:	10.1016/j.jclepro.2018.10.243
显示于类别:	[水資源及環境工程學系暨研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	256	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....