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注意:仅列出部分参考文献,下载原文获取全部文献信息。Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts
Yanlai Zhou et al.
JOURNAL OF CLEANER PRODUCTION (2019)
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction
Congcong Wen et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2019)
Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction
Jiachen Zhao et al.
CHEMOSPHERE (2019)
A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory
Yanlin Qi et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2019)
A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration
Dongming Qin et al.
IEEE ACCESS (2019)
A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction
Junshan Wang et al.
NEUROCOMPUTING (2018)
Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation
Xiang Li et al.
ENVIRONMENTAL POLLUTION (2017)
Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5
Bun Theang Ong et al.
NEURAL COMPUTING & APPLICATIONS (2016)
Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation
Ying Zhang et al.
REMOTE SENSING OF ENVIRONMENT (2015)
Time series forecasting using a deep belief network with restricted Boltzmann machines
Takashi Kuremoto et al.
NEUROCOMPUTING (2014)
Particulate Matter Air Pollution and Cardiovascular Disease An Update to the Scientific Statement From the American Heart Association
Robert D. Brook et al.
CIRCULATION (2010)
Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models
Srinivasa Ravi Chandra et al.
Journal of Intelligent Transportation Systems (2009)
Increase in tropospheric nitrogen dioxide over China observed from space
A Richter et al.
NATURE (2005)
Comparison of parametric and nonparametric models for traffic flow forecasting
BL Smith et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2002)