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Air pollution prediction based on factory-aware attentional LSTM neural network
Duen Ren Liu
*
, Yi Kuan Hsu, Hsing Yu Chen, Huan Jian Jau
*
此作品的通信作者
資訊管理研究所
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同行評審
28
引文 斯高帕斯（Scopus）
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Keyphrases
Prediction Method
100%
Particulate Matter 2.5 (PM2.5)
100%
Long Short-term Memory Neural Network
100%
Air Pollution Prediction
100%
PM2.5 Concentration
83%
Air Quality
50%
Local Area
33%
Station Data
33%
Attention Mechanism
33%
Neighbouring Area
33%
Air Pollution Data
33%
Industrial Data
33%
PM2.5 Pollution
33%
Human Health
16%
Prediction Accuracy
16%
High Concentration
16%
Heterogeneous Data
16%
Experimental Performance Evaluation
16%
Performance Prediction
16%
Negative Effects
16%
Spatial Relationship
16%
Aerodynamic Diameter
16%
Quality Issues
16%
Long Short-term Memory Model
16%
Long-term Exposure
16%
Area Data
16%
Air Pollution
16%
Pollutants
16%
Spatial Transformation
16%
Particulate Matter
16%
Attention Weight
16%
Monitoring Station
16%
Industrial Emissions
16%
Factory Data
16%
Monitoring Area
16%
Hidden Features
16%
Global Concern
16%
Air Pollution Problem
16%
Air Quality Sensors
16%
Air Quality Management
16%
Industrial Factors
16%
Engineering
Particular Matter 2.5
100%
Long Short-Term Memory
100%
Air Pollution
100%
Air Quality
38%
Local Area
15%
Industrial Data
15%
Negative Effect
7%
Research Work
7%
Atmospheric Aerosol
7%
Aerodynamic Diameter
7%
Quality Information
7%
Quality Issue
7%
Human Health
7%
Prediction Performance
7%
Spatial Transformation
7%
Computer Science
Long Short-Term Memory Neural Network
100%
Air Pollution
100%
Attention (Machine Learning)
33%
Prediction Accuracy
16%
Heterogeneous Data
16%
Spatial Relationship
16%
Prediction Performance
16%
Long Short-Term Memory Network
16%
Memory Model
16%
Negative Effect
16%
Spatial Transformation
16%
Information Quality
16%
Monitoring Station
16%
Local Data Area
16%
Quality Management
16%