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

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...

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Veröffentlicht in:	Journal of cleaner production Jg. 209; S. 134 - 145
Hauptverfasser:	Zhou, Yanlai, Chang, Fi-John, Chang, Li-Chiu, Kao, I-Feng, Wang, Yi-Shin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2019
Schlagworte:	accidents air pollution Air quality algorithms Artificial intelligence (AI) Deep learning environmental management monitoring Multi-output LSTM Multi-step-ahead forecast neurons nitrogen oxides particulates Taipei city Taiwan time series analysis Taiwan Deep learning Multi-step-ahead forecast Air quality Taipei city Artificial intelligence (AI) Multi-output LSTM
ISSN:	0959-6526, 1879-1786
Online-Zugang:	Volltext
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