Posters

NP4.3
Big data and machine learning in geosciences 

This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, including deep learning and other advanced approaches
• Visualization and visual analytics of big data
• Informatics and data science
• Emerging big data paradigms, such as datacubes

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Co-organized as AS5.20/CL5.25/ESSI2.3/GD8.5/HS3.5/NH11.11/SM7.8
Convener: Mikhail Kanevski | Co-conveners: Peter Baumann, Sandro Fiore, Kwo-Sen Kuo, Nicolas Younan
Orals
| Mon, 08 Apr, 10:45–12:30, 14:00–18:00
 
Room L3
Posters
| Attendance Tue, 09 Apr, 10:45–12:30
 
Hall X4

Attendance time: Tuesday, 9 April 2019, 10:45–12:30 | Hall X4

Chairperson: Mikhail Kanevski, Peter Baumann, Sandro Fiore, Kwo-Sen Kuo, Nicolas Younan
X4.227 |
EGU2019-4886
Feature selection using simple and efficient machine learning models. Case studies and software tools
Federico Amato, Fabian Guignard, and Mikhail Kanevski
X4.228 |
EGU2019-1430
Tropical cyclone cloud coverage segmentation using convolutional neural networks with combined input parameters
(withdrawn)
Kien Nguyen, Elizabeth Ritchie, and Scott Tyo
X4.229 |
EGU2019-1621
Spatial autocorrelation in geoscientific machine learning applications: moving from data reproduction to spatial prediction
Hanna Meyer, Christoph Reudenbach, and Thomas Nauss
X4.230 |
EGU2019-2148
Identifying soil degradation in Swiss alpine grasslands using different machine learning approaches
Lauren Zweifel, Maxim Samarin, Katrin Meusburger, and Christine Alewell
X4.231 |
EGU2019-5649
Extrapolation and interpolation of climate analogues using Gaussian Processes
Eduardo Zorita and Marlene Klockmann
X4.232 |
EGU2019-7105
Rapid, catchment scale mapping of supraglacial lakes in Greenland using Google Earth Engine
James Lea and Stephen Brough
X4.233 |
EGU2019-7551
Using Neural Networks to Predict Subsurface Reservoir Properties
(withdrawn)
David Taylor, David Hodgetts, and Ginny-Marie Bradley
X4.234 |
EGU2019-7798
Deep learning of remote sensing images for large ships recognition on water surface
Shuren Chou and Bin Chen
X4.235 |
EGU2019-8062
Comparison of Diffraction Imaging Using Plane-Wave Destruction, Image Segmentation, and Multi-Domain Diffraction Identification by Deep Learning
Brydon Lowney, Ivan Lokmer, Christopher J Bean, Gareth Shane O'Brien, and Michael Igoe
X4.236 |
EGU2019-9626
A systematic assessment of machine learning models for short-term water level prediction
Abdou Khouakhi and Ian Pattison
X4.237 |
EGU2019-10640
A big data approach to estimate available roof area for solar PV installation at national scale
Alina Walch, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini
X4.238 |
EGU2019-9643
Cloud masking with Deep Learning applied to Landsat-8
Alistair Francis, Panagiotis Sidiropoulos, and Jan-Peter Muller
X4.239 |
EGU2019-9889
| Highlight
Geospatial big data analytics for agriculture using IBM-PAIRS
Anne Jones, Blair Edwards, Katharina Reusch, and Paolo Fraccaro
X4.240 |
EGU2019-12111
Well log lithology classification using supervised and unsupervised machine learning
(withdrawn)
Nadima Dwihusna, Mohamed Mohamed, and Xiaoyu Zhu
X4.241 |
EGU2019-18467
DATALAKES - data platform and stochastic Bayesian forecasting for Swiss lakes using supercomputers
Artur Safin, Damien Bouffard, Fotis Georgatos, Eric Bouillet, Fernando Perez Cruz, Alfred Wüest, Siddhartha Mishra, and Jonas Sukys
X4.242 |
EGU2019-11555
UAV image classification of a riverine landscape by using machine learning with image processing techniques
Hitoshi Miyamoto, Akito Momose, Shuji Iwami, Takuya Sato, and Takayuki Nagaya
X4.243 |
EGU2019-12522
Associating Blizzards with Corresponding Extratropical Cyclones as an Example of Phenomenon Hierarchies
Kwo-Sen Kuo, Mike Bauer, Michael Rilee, and Amidu Oloso
X4.244 |
EGU2019-6226
A Random Forest Approach for Cloud Base Analysis
Chao Li
X4.245 |
EGU2019-2215
Random Forest Algorithm for Wetland Inundation Prediction in a Semi-arid Floodplain
Sara Shaeri Karimi, Neil Saintilan, and Li Wen
X4.246 |
EGU2019-17171
Development of ground-motion prediction equations from a weighted mixture model with data-driven weights
Sebastian von Specht and Fabrice Cotton
X4.247 |
EGU2019-17274
A subspace clustering algorithm with spatial regularization for mineral mapping
Kasra Rafiezadeh Shahi, Mahdi Khodadadzadeh, Raimon Tolosana-Delgado, and Richard Gloaguen
X4.248 |
EGU2019-14204
Managing Earth Observation datasets as multidimensional arrays using SciDB and open standards
Maurin Radtke, Benedikt Graeler, Eike Hinderk Jürrens, and Simon Jirka
X4.249 |
EGU2019-14358
Artificial Neural Networks to interpret CPTs for geological modelling
(withdrawn)
Jan Gunnink, Denise Maljers, Mattijs Borst, and Bert de Doelder
X4.250 |
EGU2019-507
Dimensionality reduction techniques for simulations of the spectral radiance in the Hartley-Huggins band
Ana del Águila, Dmitry S. Efremenko, Víctor Molina García, and Jian Xu
X4.251 |
EGU2019-3367
Using SOM neural network to improve land use and cover training samples from satellite image time series
(withdrawn)
Karine Reis Ferreira, Lorena Alves Santos, Gilberto Camara, and Michelle Cristina Araujo Picoli
X4.252 |
EGU2019-17683
Optimal hierarchical clustering of multivariate data from X-band Doppler radar.
Maryna Lukach