scholarly journals Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data

2021 ◽  
Author(s):  
Pratyush Muthukumar ◽  
Emmanuel Cocom ◽  
Kabir Nagrecha ◽  
Dawn Comer ◽  
Irene Burga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollution ◽  
Big Data ◽  
Deep Learning ◽  
Meteorological Data ◽  
Atmospheric Air ◽  
Remote Sensing Satellite

scholarly journals APPLICATION OF OPEN AIR MODEL (R PACKAGE) TO ANALYZE AIR POLLUTION DATA

2017 ◽  
Vol 1 (1) ◽  
pp. 94 ◽  
Author(s):  
Intan Agustine ◽  
Hernani Yulinawati ◽  
Endro Suswantoro ◽  
Dodo Gunawan
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Air Quality ◽  
Meteorological Data ◽  
Ambient Air ◽  
Quality Data ◽  
Fluctuation Analysis ◽  
Ambient Air Quality ◽  
Long Time ◽  
Air Quality Data

Air pollution problem is faced by many countries in the world. Ambient air quality studies and monitoring need a long time period of data to cover various atmospheric conditions, which create big data. A tool is needed to make easier and more effective to analyze big data. <strong>Aims: </strong>This study aims to analyze various application of <em>openair</em> model, which is available in open-source, for analyzing urban air quality data. <strong>Methodology and results: </strong>Each pollutant and meteorological data were collected through their sampling-analysis methods (active, passive or real-time) from a certain period of time. The data processed and imported in the <em>openair</em> model were presented in <em>comma separated value</em> (csv) format. The input data must consist of date-time, pollutant, and meteorological data. The analysis is done by selecting six functions: <em>theilSen</em> for trend analysis, <em>timeVariation</em> for temporal variations, <em>scatterPlot</em> for linear correlation analysis,<em> timePlot</em> for fluctuation analysis, <em>windRose</em> for wind rose creation, and <em>polarPlot</em> for creating pollution rose. Results from these functions are discussed. <strong>Conclusion, significance and impact study: </strong><em>Openair</em> model is capable of analyzing a long time air quality data. Application of <em>openair</em> model is possible to cities in Indonesia that already monitor ambient air quality but have not analyzed the data yet

scholarly journals Air-pollution prediction in smart city, deep learning approach

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Abdellatif Bekkar ◽  
Badr Hssina ◽  
Samira Douzi ◽  
Khadija Douzi
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Urban Areas ◽  
Historical Data ◽  
Meteorological Data ◽  
Predictive Performance ◽  
The Past ◽  
Life Threatening ◽  
The Difference ◽  
Beijing China

AbstractOver the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than $$2.5 \mu m$$ 2.5 μ m ($$PM_{2.5}$$ P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $$PM_{2.5}$$ P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $$PM_{2.5}$$ P M 2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $$PM_{2.5}$$ P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $$PM_{2.5}$$ P M 2.5 concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method “hybrid CNN-LSTM multivariate” enables more accurate predictions than all the listed traditional models and performs better in predictive performance.

Heterogeneous Parallel Processing Enabled Deep Learning Pattern Recognition of Seismic Big Data in Syrna and Kandelioussa

2021 ◽  
Author(s):  
Antonios Konstantaras ◽  
Theofanis Frantzeskakis ◽  
Emmanouel Maravelakis ◽  
Alexandra Moshou ◽  
Panagiotis Argyrakis
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Pattern Recognition ◽  
Big Data ◽  
Deep Learning ◽  
Parallel Processing ◽  
Seismic Data ◽  
Seismic Activity ◽  
Point Cloud ◽  
Deep Learning Neural Network

&lt;p&gt;This research aims to depict ontological findings related to topical seismic phenomena within the Hellenic-Seismic-Arc via deep-data-mining of the existing big-seismological-dataset, encompassing a deep-learning neural network model for pattern recognition along with heterogeneous parallel processing-enabled interactive big data visualization. Using software that utilizes the R language, seismic data were 3D plotted on a 3D Cartesian plane point cloud viewer for further investigation of the formed three-dimensional morphology. As a means of mining information from seismic big data, a deep neural network was trained and refined for pattern recognition and occurrence manifestation attributes of seismic data of magnitudes greater than Ms 4.0. The deep learning neural network comprises of an input layer with six input neurons for the insertion of year, month, day, latitude, longitude and depth, followed by six hidden layers with a hundred neurons each, and one output layer of the estimated magnitude level. This approach was conceptualised to investigate for topical patterns in time yielding minor, interim and strong seismic activity, such as the one depicted by the deep learning neural network, observed in the past ten years on the region between Syrna and Kandelioussa. This area&amp;#8217;s coordinates are around 36,4 degrees in latitude and 26,7 degrees in longitude, with the deep learning neural network achieving low error rates, possibly depicting a pattern in seismic activity.&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Axaridou A., I. Chrysakis, C. Georgis, M. Theodoridou, M. Doerr, A. Konstantaras, and E. Maravelakis. 3D-SYSTEK: Recording and exploiting the production workflow of 3D-models in cultural heritage. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 51-56, 2014.&lt;/p&gt;&lt;p&gt;Konstantaras A. Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone. Informatics, 7 (4), 39, 2020.&lt;/p&gt;&lt;p&gt;Konstantaras A.J. Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters. Earth Science Informatics. 9 (1), 95-100, 2016.&lt;/p&gt;&lt;p&gt;Konstantaras A.J. Classification of distinct seismic regions and regional temporal modelling of seismicity in the vicinity of the Hellenic seismic arc. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6 (4), 1857-1863, 2012.&lt;/p&gt;&lt;p&gt;Konstantaras A., F. Vallianatos, M.R. Varley, J.P. Makris. Soft-Computing modelling of seismicity in the southern Hellenic Arc. IEEE Geoscience and Remote Sensing Letters, 5 (3), 323-327, 2008.&lt;/p&gt;&lt;p&gt;Konstantaras A., M.R. Varley, F. Vallianatos, G. Collins and P. Holifield. Recognition of electric earthquake precursors using neuro-fuzzy methods: methodology and simulation results. Proc. IASTED Int. Conf. Signal Processing, Pattern Recognition and Applications (SPPRA 2002), Crete, Greece, 303-308, 2002.&lt;/p&gt;&lt;p&gt;Maravelakis E., Konstantaras A., Kilty J., Karapidakis E. and Katsifarakis E. Automatic building identification and features extraction from aerial images: Application on the historic 1866 square of Chania Greece. 2014 International Symposium on Fundamentals of Electrical Engineering (ISFEE), Bucharest, 1-6, 2014. doi: 10.1109/ISFEE.2014.7050594.&lt;/p&gt;&lt;p&gt;Maravelakis E., A. Konstantaras, K. Kabassi, I. Chrysakis, C. Georgis and A. Axaridou. 3DSYSTEK web-based point cloud viewer. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 262-266, 2014.&lt;/p&gt;&lt;p&gt;Maravelakis E., Bilalis N., Mantzorou I., Konstantaras A. and Antoniadis A. 3D modelling of the oldest olive tree of the world. International Journal Of Computational Engineering Research. 2 (2), 340-347, 2012.&lt;/p&gt;

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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