Virtual Special Issue: Emerging Trends in Big Data Analytics and Natural Disasters Computers & Geosciences

  in Special Issue   Posted on March 6, 2020

Information for the Special Issue

Submission Deadline: Tue 01 Sep 2020
Journal Impact Factor : 2.533
Journal Name : Computers and Geosciences
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/computers-and-geosciences/call-for-papers/emerging-trends-in-big-data-analytics-and-natural-disasters
Journal & Submission Website: https://www.journals.elsevier.com/computers-and-geosciences

Special Issue Call for Papers:

Virtual Special Issue: Emerging Trends in Big Data Analytics and Natural Disasters

Computers & Geosciences

Call for papers

The analysis of massive data is one of the most challenging tasks that data scientists are facing nowadays. Much effort is currently being put into the development of new approaches that can extract useful information from huge datasets in a way which is both efficient and effective. Geosciences is one of the disciplines that is benefiting the most from these advances since the processing of high-resolution satellite, aerial images, very large time series or even the information fusion of several sources are leading to the development of very powerful models.

Natural disasters are extreme and unexpected threats resulting from natural processes of the Earth that can cause enormous human and economic losses. Among these destructive events, earthquakes, tsunamis, volcanic eruptions, hurricanes, tornadoes and floods stand out. Their prediction and characterization have been addressed from many different points of view but, due to the emerging big data techniques, much research is currently being conducted in this field. Hence, automated machine learning and deep learning methods for extracting relevant patterns, high performance computing or data visualization are being widely and successfully applied to many geoinformatics-related issues.

Although it is almost impossible to prevent natural disasters, several actions can be taken to mitigate their effects and minimize casualties and economic losses. Thus, the discovery of precursory patterns or the development of predictive models may help to deploy emergency policies or trigger adequate alarms so that regions can be evacuated. Another relevant issue lies in the estimation of affected elements at risk, their corresponding damage potentials and the potential losses.

For all the aforementioned, we kindly invite the Scientific Community to contribute to this special issue, by submitting novel and original work addressing one or more of the following topics related to natural hazards, in the context of machine learning and big data:

  1. New methods for the characterization and discovery of precursory patterns.
  2. New methods for predicting the occurrence of natural disasters.
  3. New methods for risk assessment and losses estimation.
  4. Case studies describing relevant findings with a clear interest for the Scientific Community.

Finally, the authors are strongly encouraged to share codes and data to allow the experiments to be reproduced.

Guest editors

  • Francisco Martínez-Álvarez*, Data Science & Big Data Lab, Pablo de Olavide University, Spain
  • Rudolf Scitovski, Department of Mathematics, University of Osijek, Croatia
  • Cristina Rubio-Escudero, Department of Computer Science, University of Seville, Spain
  • Antonio Morales-Esteban, Department of Building Structures and Geotechnical Engineering, University of Seville, Spain

Relevant dates

  • Submission deadline: September 1st, 2020.

Please note that papers will be handled as they are submitted, so notifications will be sent just after the reports are received.

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