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Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions 期刊论文
ENVIRONMENT INTERNATIONAL, 2019, 卷号: 128, 页码: 310-323
Authors:  Li, Lianfa;  Girguis, Mariam;  Lurmann, Frederick;  Wu, Jun;  Urman, Robert;  Rappaport, Edward;  Ritz, Beate;  Franklin, Meredith;  Breton, Carrie;  Gilliland, Frank;  Habre, Rima
Favorite  |  View/Download:2/0  |  Submit date:2019/09/24
Air pollution  Nitrogen oxides  Spatiotemporal variability  Generalization  Machine learning  Cluster methods  
Identification of the Debris Flow Process Types within Catchments of Beijing Mountainous Area 期刊论文
WATER, 2019, 卷号: 11, 期号: 4, 页码: 26
Authors:  Wang, Nan;  Cheng, Weiming;  Zhao, Min;  Liu, Qiangyi;  Wang, Jing
Favorite  |  View/Download:1/0  |  Submit date:2019/09/24
debris flow process  machinelearning  catchment  Beijing mountainous area  
Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models 期刊论文
ENVIRONMENTAL MODELLING & SOFTWARE, 2019, 卷号: 114, 页码: 112-128
Authors:  Zaherpour, Jamal;  Mount, Nick;  Gosling, Simon N.;  Dankers, Rutger;  Eisner, Stephanie;  Gerten, Dieter;  Liu, Xingcai;  Masaki, Yoshimitsu;  Schmied, Hannes Mueller;  Tang, Qiuhong;  Wada, Yoshihide
Favorite  |  View/Download:10/0  |  Submit date:2019/05/22
Machine learning  Model weighting  Gene expression programming  Global hydrological models  Optimisation  
Water Resources Assessment of China's Transboundary River Basins Using a Machine Learning Approach 期刊论文
WATER RESOURCES RESEARCH, 2019, 卷号: 55, 期号: 1, 页码: 632-655
Authors:  Yan, Jiabao;  Jia, Shaofeng;  Lv, Aifeng;  Zhu, Wenbin
Favorite  |  View/Download:21/0  |  Submit date:2019/05/22
water resources  runoff coefficient  machine learning  transboundary river  China  
Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing-Tianjin-Hebei Region, China 期刊论文
REMOTE SENSING, 2018, 卷号: 10, 期号: 12, 页码: 17
Authors:  Li, Lijuan;  Chen, Baozhang;  Zhang, Yanhu;  Zhao, Youzheng;  Xian, Yue;  Xu, Guang;  Zhang, Huifang;  Guo, Lifeng
Favorite  |  View/Download:12/0  |  Submit date:2019/05/23
daily PM2  5concentrations  remote sensing  MODIS AOD  machine learning algorithm  spatial and temporal distribution  
Predicting multiple land use transitions under rapid urbanization and implications for land management and urban planning: The case of Zhanggong District in central China 期刊论文
HABITAT INTERNATIONAL, 2018, 卷号: 82, 页码: 48-61
Authors:  Wang, Lingzhi;  Pijanowski, Bryan;  Yang, Weishi;  Zhai, Ruixue;  Omrani, Hichem;  Li, Ke
Favorite  |  View/Download:11/0  |  Submit date:2019/05/23
Multiple land use transitions  Urbanization  Artificial neural networks  Land use management  Urban planning  China  
Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2018, 卷号: 38, 期号: 13, 页码: 4891-4902
Authors:  Wang, Bin;  Zheng, Lihong;  Liu, De Li;  Ji, Fei;  Clark, Anthony;  Yu, Qiang
Favorite  |  View/Download:7/0  |  Submit date:2019/05/23
GCMs  machine learning  multi-model ensemble  random forest  support vector machine  
Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth 期刊论文
REMOTE SENSING OF ENVIRONMENT, 2018, 卷号: 217, 页码: 573-586
Authors:  Li, Lianfa;  Zhang, Jiehao;  Meng, Xia;  Fang, Ying;  Ge, Yong;  Wang, Jinfeng;  Wang, Chengyi;  Wu, Jun;  Kan, Haidong
Favorite  |  View/Download:12/0  |  Submit date:2019/05/23
PM2.5  MAIAC AOD  High spatiotemporal resolution  Temporal variation  AOD-PM2.5 associations  Spatial effects  Missingness  Machine learning  
Spatiotemporal patterns of PM10 concentrations over China during 2005-2016: A satellite-based estimation using the random forests approach 期刊论文
ENVIRONMENTAL POLLUTION, 2018, 卷号: 242, 页码: 605-613
Authors:  Chen, Gongbo;  Wang, Yichao;  Li, Shanshan;  Cao, Wei;  Ren, Hongyan;  Knibbs, Luke D.;  Abramson, Michael J.;  Guo, Yuming
Favorite  |  View/Download:10/0  |  Submit date:2019/05/23
PM10  AOD  Random forests  China  
A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information 期刊论文
SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 卷号: 636, 页码: 52-60
Authors:  Chen, Gongbo;  Li, Shanshan;  Knibbs, Luke D.;  Hamm, N. A. S.;  Cao, Wei;  Li, Tiantian;  Guo, Jianping;  Ren, Hongyan;  Abramson, Michael J.;  Guo, Yuming
Favorite  |  View/Download:11/0  |  Submit date:2019/05/23
PM2.5  Aerosol optical depth  Random forests  Machine learning  China