暂无分享,去创建一个
Andreas Hotho | Martin Becker | Albin Zehe | Florian Lautenschlager | Konstantin Kobs | Michael Steininger | A. Hotho | Martin Becker | M. Steininger | Florian Lautenschlager | Konstantin Kobs | Albin Zehe
[1] Vikas Singh,et al. Higher Pollution Episode Detection Using Image Classification Techniques , 2016, Environmental Modeling & Assessment.
[2] B. Brunekreef,et al. Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa. , 2018, The Science of the total environment.
[3] Yong Li,et al. Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality , 2019, Journal of Cleaner Production.
[4] Hao Wu,et al. End-to-end learning for image-based air quality level estimation , 2018, Machine Vision and Applications.
[5] G. Lemasters,et al. Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. , 2017, Atmospheric environment.
[6] David Morley,et al. A land use regression variable generation, modelling and prediction tool for air pollution exposure assessment , 2018, Environmental Modelling & Software.
[7] G. Lemasters,et al. A Review of Land-use Regression Models for Characterizing Intraurban Air Pollution Exposure , 2007, Inhalation toxicology.
[8] R. D'Agostino. An omnibus test of normality for moderate and large size samples , 1971 .
[9] Jin Zhang,et al. An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting. , 2019, Chemosphere.
[10] Martina S. Ragettli,et al. Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles , 2014, Environmental health perspectives.
[11] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[12] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[13] Bert Brunekreef,et al. Satellite NO2 data improve national land use regression models for ambient NO2 in a small densely populated country , 2015 .
[14] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[15] E. S. Pearson,et al. Tests for departure from normality. Empirical results for the distributions of b2 and √b1 , 1973 .
[16] Lothar Thiele,et al. Pushing the spatio-temporal resolution limit of urban air pollution maps , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[17] Mikhail F. Kanevski,et al. Air Pollution Mapping Using Nonlinear Land Use Regression Models , 2014, ICCSA.
[18] Bert Brunekreef,et al. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project , 2013 .
[19] B. Brunekreef,et al. Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 : results of the ESCAPE project , 2012 .
[20] Derek M. Elsom,et al. Atmospheric Pollution: A Global Problem , 1992 .
[21] Md. Saniul Alam,et al. Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis , 2015, Journal of the Air & Waste Management Association.
[22] Yang Li,et al. Air Pollutant Concentration Forecast Based on Support Vector Regression and Quantum-Behaved Particle Swarm Optimization , 2018, Environmental Modeling & Assessment.
[23] Sancho Salcedo-Sanz,et al. Prediction of hourly O3 concentrations using support vector regression algorithms , 2010 .
[24] Bert Brunekreef,et al. Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project. , 2012, Environmental science & technology.
[25] Jure Leskovec,et al. Image Labeling on a Network: Using Social-Network Metadata for Image Classification , 2012, ECCV.
[26] Tianqi Chen,et al. Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.
[27] Yu Liu,et al. Autoencoder-based deep belief regression network for air particulate matter concentration forecasting , 2018, Journal of Intelligent & Fuzzy Systems.
[28] Bert Brunekreef,et al. Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation. , 2015, Environmental science & technology.
[29] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[30] David C. Carslaw,et al. Estimations of road vehicle primary NO2 exhaust emission fractions using monitoring data in London , 2005 .
[31] Jiebo Luo,et al. Using user generated online photos to estimate and monitor air pollution in major cities , 2015, ICIMCS '15.
[32] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[33] Alexander J. Smola,et al. Support Vector Regression Machines , 1996, NIPS.
[34] Guangming Zeng,et al. Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China , 2015 .
[35] Sepp Hochreiter,et al. Self-Normalizing Neural Networks , 2017, NIPS.
[36] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[37] J. Gulliver,et al. A review of land-use regression models to assess spatial variation of outdoor air pollution , 2008 .
[38] A. Azzouz. 2011 , 2020, City.
[39] Qi Li,et al. A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN , 2017 .
[40] Jiansheng Wu,et al. Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China , 2015, Environmental Science and Pollution Research.
[41] M. Adams. ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING , 2015 .
[42] Radu Horaud,et al. A Comprehensive Analysis of Deep Regression , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[44] A. James. 2010 , 2011, Philo of Alexandria: an Annotated Bibliography 2007-2016.
[45] Chen Qiu,et al. Rewarding Coreference Resolvers for Being Consistent with World Knowledge , 2019, EMNLP/IJCNLP.
[46] Kaiming He,et al. Exploring the Limits of Weakly Supervised Pretraining , 2018, ECCV.
[47] A. Buevich,et al. Modeling of surface dust concentrations using neural networks and kriging , 2016 .
[48] Yan Zhang,et al. A land use regression model for estimating the NO2 concentration in Shanghai, China. , 2015, Environmental research.
[49] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[50] Lei Huang,et al. Development of land use regression models for PM2.5, SO2, NO2 and O3 in Nanjing, China , 2017, Environmental research.
[51] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[52] Sebastian Ruder,et al. Universal Language Model Fine-tuning for Text Classification , 2018, ACL.
[53] Alexandra Schneider,et al. Land use regression modeling of ultrafine particles, ozone, nitrogen oxides and markers of particulate matter pollution in Augsburg, Germany. , 2017, The Science of the total environment.
[54] M. Green. Air pollution and health , 1995 .