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NTIS 바로가기Environmental modelling & software : with environment data news, v.124, 2020년, pp.104600 -
Zhang, Bo (Corresponding author.) , Zhang, Hanwen (Corresponding author.) , Zhao, Gengming , Lian, Jie
Abstract Air pollution problems have a severe effect on the natural environment and public health. The application of machine learning to air pollutant data can result in a better understanding of environmental quality. Of these methods, the deep learning method has proven to be a very efficient an...
J. Hazard Mater. Akyuz 170 1 13 2009 10.1016/j.jhazmat.2009.05.029 Meteorological variations of PM2.5/PM10 concentrations and particle-associated polycyclic aromatic hydrocarbons in the atmospheric environment of Zonguldak, Turkey
Almousli 2013 International Conference on Neural Information Processing Semi supervised autoencoders: better focusing model capacity during feature extraction
Environ. Model. Softw Brooks 76 81 2016 10.1016/j.envsoft.2015.10.012 Predicting recreational water quality advisories: a comparison of statistical methods
J. Air Waste Manag. Assoc. Cardelino 51 8 1227 2001 10.1080/10473289.2001.10464342 Ozone predictions in Atlanta, Georgia: analysis of the 1999 ozone season
Environ. Model. Softw Carslaw 27 52 2012 10.1016/j.envsoft.2011.09.008 Openair-an R package for air quality data analysis
Atmos. Environ. Chen 92 182 2014 10.1016/j.atmosenv.2014.04.030 Seasonal modeling of PM2.5 in California\"s san Joaquin valley
Expert Syst. Appl. Chen 72 221 2017 10.1016/j.eswa.2016.10.065 Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
Collobert 2008 Proceedings of the 25th International Conference on Machine Learning A unified architecture for natural language processing: deep neural networks with multitask learning
Atmos. Environ. Degaetano 38 11 1547 2004 10.1016/j.atmosenv.2003.12.020 Temporal, spatial and meteorological variations in hourly PM2.5 concentration extremes in New York City
J. Electr. Comput. Eng. Deters 2017 2017 Modeling PM2. 5 urban pollution using machine learning and selected meteorological parameters
Neurocomputing Guo 187.C 27 2016 10.1016/j.neucom.2015.09.116 Deep learning for visual understanding: a review
Biometrika Hannan 71 2 273 1984 10.1093/biomet/71.2.273 A method for autoregressive-moving average estimation
He 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Deep residual learning for image recognition
Science Hinton 313 5786 504 2006 10.1126/science.1127647 Reducing the dimensionality of data with neural networks
Geophys. Res. Lett. Hsu 37 7 256 2010 10.1029/2009GL042243 Global long-lived chemical modes excited in a 3-D chemistry transport model: stratospheric N2O, NOy, O3 and CH4 chemistry
Sensors Huang 18 7 2220 2018 10.3390/s18072220 A deep cnn-lstm model for particulate matter (PM2. 5) forecasting in smart cities
Environ. Model. Softw Johansson 64 143 2015 10.1016/j.envsoft.2014.11.021 Fusion of meteorological and air quality data extracted from the web for personalized environmental information services
Environ. Pollut. Kampa 151 2 362 2008 10.1016/j.envpol.2007.06.012 Human health effects of air pollution
Int. J. Hyg Environ. Health Kappos 207 4 399 2004 10.1078/1438-4639-00306 Health effects of particles in ambient air
Int. J. Environ. Pollut. Karaca 28 3/4 310 2006 10.1504/IJEP.2006.011214 NN-AirPol: a neural-networks-based method for air pollution evaluation and control
Environ. Model. Softw Kiesewetter 74 201 2015 10.1016/j.envsoft.2015.02.022 Modelling PM2. 5 impact indicators in Europe: health effects and legal compliance
J. Hazard Mater. Kim 154 1-3 440 2008 10.1016/j.jhazmat.2007.10.042 Spatial distribution of particulate matter (PM10 and PM2. 5) in Seoul Metropolitan Subway stations
Krizhevsky 2011 Using Very Deep Autoencoders for Content-Based Image Retrieval
Neurocomputing Kuremoto 137 47 2014 10.1016/j.neucom.2013.03.047 Time series forecasting using a deep belief network with restricted Boltzmann machines
Lange 2010 The 2010 International Joint Conference on Neural Networks (IJCNN) Deep auto-encoder neural networks in reinforcement learning
Lancet Lim 380 9859 2224 2012 10.1016/S0140-6736(12)61766-8 A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010
Lin 2017 Proceedings of the 3rd Workshop on Noisy User-Generated Text Multi-channel bilstm-crf model for emerging named entity recognition in social media
J. Geophys. Res.: Atmosphere Liu 109 D22 2004 Mapping annual mean groundlevel PM2. 5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States
Atmos. Environ. Liu 44 20 2415 2010 10.1016/j.atmosenv.2010.03.035 Understanding of regional air pollution over China using CMAQ, part I performance evaluation and seasonal variation
Liu 2017 NIRS Feature Extraction Based on Deep Auto-Encoder Neural Network
Chemosphere Lu 59 5 693 2005 10.1016/j.chemosphere.2004.10.032 Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends
J. Geophys. Res.: Atmosphere McKeen 112 D10 2007 10.1029/2006JD007608 Evaluation of several PM2. 5 forecast models using data collected during the ICARTT/NEAQS 2004 field study
Meng 2017 2017 International Joint Conference on Neural Networks (IJCNN) Relational autoencoder for feature extraction
Mohamed 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) Deep Bi-directional Recurrent Networks Over Spectral Windows
Speech Commun. Ogawa 89 70 2017 10.1016/j.specom.2017.02.009 Error detection and accuracy estimation in automatic speech recognition using deep bidirectional recurrent neural networks
Ong 2014 Big Data (Big Data), 2014 IEEE International Conference on Dynamic pre-training of deep recurrent neural networks for predicting environmental monitoring data
Meas. Control Technol. QIN 2 2017 Combination of 3D CNNs and LSTMs and its application in activity recognition
Atmos. Environ. Saide 45 16 2769 2011 10.1016/j.atmosenv.2011.02.001 Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model
Atmos. Environ. Part B - Urban Atmos. Scheffe 27 1 23 1993 10.1016/0957-1272(93)90043-6 A review of the development and application of the urban airshed model
Advances in Neural Information Processing Systems Scholkopf 19 153 2007 Greedy Layer-Wise Training of Deep Networks
Mech. Syst. Signal Process. Shao 95 187 2017 10.1016/j.ymssp.2017.03.034 A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
AGU Fall Meet. Abstr. So 2014 Relationship of regional PM2. 5 variations in east Asia and climate variability in the North Pacific
Environ. Model. Softw Solazzo 24 3 381 2009 10.1016/j.envsoft.2008.08.001 Improved parameterisation for the numerical modelling of air pollution within an urban street canyon
Adv. Neural Inf. Process. Syst. Stollenga 2015 Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation
J. Environ. Manag. Sun 188 144 2017 10.1016/j.jenvman.2016.12.011 Daily PM2. 5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm
Sundermeyer 2012 Thirteenth Annual Conference of the International Speech Communication Association LSTM neural networks for language modeling
Environ. Model. Softw Taylor 22 6 797 2007 10.1016/j.envsoft.2006.03.002 Environmental time series analysis and forecasting with the Captain toolbox
J. Biomed. Inform. Tutubalina 2018 10.1016/j.jbi.2018.06.006 Medical concept normalization in social media posts with recurrent neural networks
Ecol. Model. Viotti 148 1 27 2002 10.1016/S0304-3800(01)00434-3 Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia
Water Air Soil Pollut. Wang 130 1-4 391 2001 10.1023/A:1013833217916 A nested air quality prediction modeling system for urban and regional scales: application for high-ozone episode in Taiwan
Xi 2015 Service Operations and Logistics, and Informatics (SOLI), 2015 IEEE International Conference on A comprehensive evaluation of air pollution prediction improvement by a machine learning method
J. Signal Process. Syst. Xu 1 2017 A bidirectional LSTM approach with word embeddings for sentence boundary detection
Optik Zhao 158 266 2017 10.1016/j.ijleo.2017.12.038 Applying deep bidirectional LSTM and mixture density network for basketball trajectory prediction
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