Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions-Reference-Cited by-同舟云学术

Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions

Published:2019-07 Issue: Volume:128 Page:310-323
ISSN:0160-4120
Container-title:Environment International
language:en
Short-container-title:Environment International

Author:

Li Lianfa,Girguis Mariam,Lurmann Frederick,Wu Jun^ORCID,Urman Robert,Rappaport Edward^ORCID,Ritz Beate,Franklin Meredith,Breton Carrie,Gilliland Frank,Habre Rima

Funder

Lifecourse Approach to Developmental Repercussions of Environmental Agents on Metabolic and Respiratory Health NIH ECHO

Southern California Environmental Health Sciences Center

National Natural Science Foundation of China

National Institute of Environmental Health Sciences

California Air Resources Board

Publisher

Elsevier BV

Subject

General Environmental Science

Reference44 articles.

1. A geographically and temporally weighted regression model for ground-level PM2.5 estimation from satellite-derived 500 m resolution AOD;Bai;Remote Sens.,2016

2. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe e the ESCAPE project;Beelen;Atmos. Environ.,2013

3. Random Search for Hyper-Parameter Optimization Machine Learning Research;Bergstra,2012

4. Pattern Recognition and Machine Learning: Springer;Bishop,2006

5. Statistic notes: regression towards the mean;Bland;Br. Med. J.,1994

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence for improving Nitrogen Dioxide forecasting of Abu Dhabi environment agency ground-based stations;Journal of Big Data;2023-06-02

2. Machine Learning in Environmental Exposure Assessment;Machine Learning in Chemical Safety and Health;2022-10-21

3. Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid;PLOS ONE;2022-06-01

4. Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network;International Journal of Computational Intelligence and Applications;2022-06

5. Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis;Environmental Modelling & Software;2022-03