[DMANET] CFP - Elsevier Journal of "Environmental Modelling & Software" journal, special Issue on "Machine Learning Advances Environmental Science"

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Environmental Modelling & Software
Official Journal of the International Environmental Modelling & Software
Society

Special Issue: Machine Learning Advances Environmental Science
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|>>
https://www.journals.elsevier.com/environmental-modelling-and-software/call-for-papers/machine-learning-advances<<|

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Aim & Scope
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Environmental data are growing steadily in volume, complexity and
diversity to Big Data, mainly driven by advanced sensor technology.
Machine Learning offers new techniques for unravelling complexity and
knowledge discovery from Big Data in environmental sciences.

The aim of the SI is to provide a state-of-the-art survey of
environmental research topics that can benefit from Machine Learning
methods and techniques.

To this purpose, the SI welcomes papers on successful environmental
applications of machine learning and pattern recognition techniques to
diverse domains of environmental research, that demonstrate how Machine
Learning improves our understanding of natural systems,
socio-environmental interactions, or tackling the inherent complexity of
environmental Big Data. Application domains may vary, and include for
instance recognition of biodiversity in thermal, photo and acoustic
images, natural hazards analysis and prediction, environmental remote
sensing, estimation of environmental risks, prediction of the
concentrations of pollutants in geographical areas, environmental
threshold analysis and predictive modelling, estimation of Genetical
Modified Organisms (GMO) effects on non-target species. Contributions
are expected to have a strong methodological contribution to
environmental sciences research, and applications of known methods in
new case studies will not be considered.

The SI offers a place for Machine Learning and Environmental research
communities to interact, and demonstrate the advances of Machine
Learning for the Environmental Sciences. Prospective contributions
should clearly indicate their contribution in tackling open problems in
environmental research that still have not properly benefited from
Machine Learning.

The SI is inspired by the first Workshop on Machine Learning Advances
Environmental Science (MAES) held at International Conference on Pattern
Recognition (ICPR) 2020, held on January 10-15, 2021.

Authors should consult the general author guidelines of the journal [1]
and submit their articles through the Editorial Manager submission
system [2].
When submitting the manuscript, select as article type
"VSI-Mach.Learn.Adv.Env.Sc".
[1]:
https://www.elsevier.com/journals/environmental-modelling-and-software/1364-8152/guide-for-authors

[2]: https://www.editorialmanager.com/envsoft/default.aspx

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Timetable
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01 Feb 2021 - Open for submissions
01 July 2021 - ***Submission deadline***
July-August 2021 - Author notifications & revisions
September 2021 - Final editorial decisions
December 2021 - Publication

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Editor-in-Chief
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D.P. Ames, Brigham Young University, Provo, Utah, United States

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Guest Editors
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Ioannis N. Athanasiadis, Wageningen University and Research, The
Netherlands
Francesco Camastra, University of Naples Parthenope, Italy
Friedrich Recknagel, University of Adelaide, Australia
Antonino Staiano, University of Naples Parthenope, Italy


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