becker > quality | BibSonomy

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1senseBox | Die Kiste mit Sinn
https://sensebox.de/
6 лет назад , @becker
sensor
sensorbox
air
quality
box
p2map
eva
everyaware
pollution
project
sensorsensorboxairqualityboxp2mapevaeveryawarepollutionproject
(0)
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- Запись сообщества
- посмотреть историю записи
2Feinstaubalarm in Stuttgart? | Live-Messwerte Stuttgart & Region - Stuttgarter Zeitung
https://scicar409635912018.sched.com/event/FmNX
6 лет назад , @becker
air
data
dataset
eva
everyaware
germany
p2map
quality
set
airdatadatasetevaeveryawaregermanyp2mapqualityset
(0)
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- Запись сообщества
- посмотреть историю записи
1Air Quality Measures on the National Environmental Health Tracking Network - Data.gov
https://catalog.data.gov/dataset/air-quality-measures-on-the-national-environmental-health-tracking-network
6 лет назад , @becker
air
quality
p2map
data
set
dataset
gov
usa
eva
everyaware
airqualityp2mapdatasetdatasetgovusaevaeveryaware
(0)
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- Запись сообщества
- посмотреть историю записи
1London Air Quality Network » Annual Pollution Maps
https://www.londonair.org.uk/london/asp/annualmaps.asp
6 лет назад , @becker
p2map
air
quality
map
london
model
data
dataset
p2mapairqualitymaplondonmodeldatadataset
(0)
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- Запись сообщества
- посмотреть историю записи
1Luftverschmutzung in Stadtring Süd, Würzburg: Echtzeit-Karte des Luftqualitätsindex
http://aqicn.org/map/germany/bayern/wurzburg/stadtring-sud/de/
6 лет назад , @becker
air
quality
index
aqi
p2map
eva
everyaware
airqualityindexaqip2mapevaeveryaware
(0)
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- Запись сообщества
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14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
F. Lautenschlager, M. Becker, K. Kobs, M. Steininger, P. Davidson, A. Krause, и A. Hotho. Atmospheric Environment, (2020)
4 лет назад , @becker
air
automl
bayes
eva
everyaware
land
lur
myown
open
optimization
project:bmbf
quality
regression
use
airautomlbayesevaeveryawarelandlurmyownopenoptimizationproject:bmbfqualityregressionuse
(0)
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12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images
M. Steininger, K. Kobs, A. Zehe, F. Lautenschlager, M. Becker, и A. Hotho. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6 (3): 1--24 (2020)
4 лет назад , @becker
air
cnn
image
map
mapping
myown
network
networks
neural
open
p21
prediction
project:bmbf
quality
street
aircnnimagemapmappingmyownnetworknetworksneuralopenp21predictionproject:bmbfqualitystreet
(0)
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1Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities---a case study in Sheffield
S. Munir, M. Mayfield, D. Coca, S. Jubb, и O. Osammor. Environmental Monitoring and Assessment, 191 (2): 94 (22.01.2019)
5 лет назад , @becker
air
emote
eva
evaluation
plolution
quality
review
sensor
sensors
test
airemoteevaevaluationplolutionqualityreviewsensorsensorstest
(0)
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3W-Air: Enabling Personal Air Pollution Monitoring on Wearables
B. Maag, Z. Zhou, и L. Thiele. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2 (1): 24 (2018)
6 лет назад , @becker
p2map
air
quality
eva
everyaware
airprobe
airprobe2
calibration
nn
neural
network
p2mapairqualityevaeveryawareairprobeairprobe2calibrationnnneuralnetwork
(0)
копироватьудалитьдобавить публикацию в буфер
2Variability in and Agreement between Modeled and Personal Continuously Measured Black Carbon Levels Using Novel Smartphone and Sensor Technologies
M. Nieuwenhuijsen, D. Donaire-Gonzalez, I. Rivas, M. de Castro, M. Cirach, G. Hoek, E. Seto, M. Jerrett, и J. Sunyer. Environmental Science & Technology, 49 (5): 2977--2982 (февраля 2015)
6 лет назад , @becker
2.5
air
bc
black
carbon
eva
map
mapping
model
p2map
pm
pm2.5
pm25
quality
school
2.5airbcblackcarbonevamapmappingmodelp2mappmpm2.5pm25qualityschool
(0)
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3A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
W. Cheng, Y. Shen, Y. Zhu, и L. Huang. AAAI, (2018)
6 лет назад , @becker
air
attention
eva
everyaware
model
network
neural
nn
p2map
quality
airattentionevaeveryawaremodelnetworkneuralnnp2mapquality
(0)
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3Machine learning and air quality modeling
C. Keller, M. Evans, J. Kutz, и S. Pawson. Big Data (Big Data), 2017 IEEE International Conference on, стр. 4570--4576. IEEE, (2017)
6 лет назад , @becker
p2map
air
quality
pollution
machine
learning
random
forest
p2mapairqualitypollutionmachinelearningrandomforest
(0)
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2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China
L. Hu, J. Liu, и Z. He. Sustainability, 8 (8): 786 (2016)
6 лет назад , @becker
p2map
eva
osm
lur
land
use
regression
open
street
map
air
quality
pollution
prediction
linear
citedby:scholar:count:4
citedby:scholar:timestamp:2018-2-21
p2mapevaosmlurlanduseregressionopenstreetmapairqualitypollutionpredictionlinearcitedby:scholar:count:4citedby:scholar:timestamp:2018-2-21
(0)
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2Land use regression models for ultrafine particles in six European areas
E. van Nunen, R. Vermeulen, M. Tsai, N. Probst-Hensch, A. Ineichen, M. Davey, M. Imboden, R. Ducret-Stich, A. Naccarati, D. Raffaele и 1 other автор(ы). Environmental science & technology, 51 (6): 3336--3345 (2017)
6 лет назад , @becker
air
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
eva
features
land
lur
map
open
osm
p2map
pollution
quality
regression
street
use
aircitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21evafeatureslandlurmapopenosmp2mappollutionqualityregressionstreetuse
(0)
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6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, и A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628-640 (2015)PMID: 25947321.
6 лет назад , @becker
air
eva
features
land
lur
map
open
osm
p2map
pollution
prediction
quality
regression
roads
street
use
airevafeatureslandlurmapopenosmp2mappollutionpredictionqualityregressionroadsstreetuse
(0)
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3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING
M. Adams. (2015)
6 лет назад , @becker
lur
land
use
regression
air
quality
pollution
neural
networks
nn
prediction
p2map
eva
lurlanduseregressionairqualitypollutionneuralnetworksnnpredictionp2mapeva
(0)
копироватьудалитьдобавить публикацию в буфер
6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, и A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628--640 (2015)
6 лет назад , @becker
lur
eva
p2map
nn
pm10
neural
network
land
use
regression
air
pollution
quality
non
parametric
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
lurevap2mapnnpm10neuralnetworklanduseregressionairpollutionqualitynonparametriccitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21
(0)
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2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models
M. Adams, и P. Kanaroglou. Journal of environmental management, (2016)
6 лет назад , @becker
lur
land
use
regression
air
pollution
quality
eva
p2map
neural
network
nn
mobile
stationary
prediction
citedby:scholar:count:31
citedby:scholar:timestamp:2018-2-21
lurlanduseregressionairpollutionqualityevap2mapneuralnetworknnmobilestationarypredictioncitedby:scholar:count:31citedby:scholar:timestamp:2018-2-21
(0)
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3A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN
J. Fan, Q. Li, J. Hou, X. Feng, H. Karimian, и S. Lin. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2017)
6 лет назад , @becker
air
deep
eva
land
lur
network
neural
nn
p2map
pollution
quality
regression
temporal
use
airdeepevalandlurnetworkneuralnnp2mappollutionqualityregressiontemporaluse
(0)
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1Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter
R. Brokamp. University of Cincinnati, (2016)
6 лет назад , @becker
air
dissertation
eva
forest
land
learning
lur
lurf
machine
p2map
pm
pollution
quality
random
regression
use
variance
airdissertationevaforestlandlearninglurlurfmachinep2mappmpollutionqualityrandomregressionusevariance
(0)
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3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches
C. Brokamp, R. Jandarov, M. Rao, G. LeMasters, и P. Ryan. Atmospheric Environment, (2017)
6 лет назад , @becker
air
citedby:scholar:count:11
citedby:scholar:timestamp:2018-2-21
comparison
eva
forest
land
learning
linear
lur
machine
ml
p2map
pollution
quality
random
regression
use
aircitedby:scholar:count:11citedby:scholar:timestamp:2018-2-21comparisonevaforestlandlearninglinearlurmachinemlp2mappollutionqualityrandomregressionuse
(0)
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2Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide
L. Spinelle, M. Gerboles, M. Villani, M. Aleixandre, и F. Bonavitacola. Sensors and Actuators B: Chemical, 215 (Supplement C): 249 - 257 (2015)
7 лет назад , @becker
air
ann
calibration
multiple
network
neural
nn
no
no2
nose
o3
p2map
pollution
quality
sensor
sensors
airanncalibrationmultiplenetworkneuralnnnono2noseo3p2mappollutionqualitysensorsensors
(0)
копироватьудалитьдобавить публикацию в буфер
3Mapping urban air quality in near real-time using observations from low-cost sensors and model information
P. Schneider, N. Castell, M. Vogt, F. Dauge, W. Lahoz, и A. Bartonova. Environment International, (2017)
7 лет назад , @becker
air
calibration
co2
eva
everyaware
multi
p2map
quality
sensor
aircalibrationco2evaeveryawaremultip2mapqualitysensor
(0)
копироватьудалитьдобавить публикацию в буфер
3Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors.
B. Maag, O. Saukh, D. Hasenfratz, и L. Thiele. EWSN, стр. 169--180. (2016)
7 лет назад , @becker
airprobe
neural
networks
multi
linear
regression
calibration
sensor
air
quality
citedby:scholar:count:3
citedby:scholar:timestamp:2017-5-12
airprobeneuralnetworksmultilinearregressioncalibrationsensorairqualitycitedby:scholar:count:3citedby:scholar:timestamp:2017-5-12
(0)
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3U-Air: When Urban Air Quality Inference Meets Big Data
Y. Zheng, F. Liu, и H. Hsieh. International Conference on Knowledge Discovery and Data Mining (SIGKDD), стр. 1436--1444. New York, NY, USA, ACM, (2013)
7 лет назад , @becker
air
citedby:scholar:count:205
citedby:scholar:timestamp:2017-2-4
diss
geo
inthesis
p2map
quality
spatial
uw_ws17_ml
aircitedby:scholar:count:205citedby:scholar:timestamp:2017-2-4dissgeointhesisp2mapqualityspatialuw_ws17_ml
(0)
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1senseBox | Die Kiste mit Sinn

2Feinstaubalarm in Stuttgart? | Live-Messwerte Stuttgart & Region - Stuttgarter Zeitung

1Air Quality Measures on the National Environmental Health Tracking Network - Data.gov

1London Air Quality Network » Annual Pollution Maps

1Luftverschmutzung in Stadtring Süd, Würzburg: Echtzeit-Karte des Luftqualitätsindex

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14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images

1Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities---a case study in Sheffield

3W-Air: Enabling Personal Air Pollution Monitoring on Wearables

2Variability in and Agreement between Modeled and Personal Continuously Measured Black Carbon Levels Using Novel Smartphone and Sensor Technologies

3A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations

3Machine learning and air quality modeling

2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China

2Land use regression models for ultrafine particles in six European areas

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models

3A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

1Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter

3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

2Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide

3Mapping urban air quality in near real-time using observations from low-cost sensors and model information

3Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors.

3U-Air: When Urban Air Quality Inference Meets Big Data

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