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<div class="moz-text-flowed" style="font-family: -moz-fixed;
font-size: 12px;" lang="x-unicode">Dear all, <br>
<br>
I have received the following CFP with the kindly request to
circulate among possible interested parties. <br>
<br>
Thanks for your cooperation, <br>
Fabio Bellavia <br>
<br>
--- <br>
<br>
apologies for multiple posting, please distribute among interested
parties <br>
==========================================================================
<br>
<br>
Environmental Modelling & Software <br>
Official Journal of the International Environmental Modelling
& Software Society <br>
<br>
Special Issue: Machine Learning Advances Environmental Science <br>
============================================================== <br>
<br>
|>> <a class="moz-txt-link-freetext"
href="https://www.journals.elsevier.com/environmental-modelling-and-software/call-for-papers/machine-learning-advances"
moz-do-not-send="true">https://www.journals.elsevier.com/environmental-modelling-and-software/call-for-papers/machine-learning-advances</a><<|<br>
<br>
============ <br>
Aim & Scope <br>
============ <br>
<br>
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. <br>
<br>
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. <br>
<br>
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. <br>
<br>
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. <br>
<br>
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. <br>
<br>
Αuthors should consult the general author guidelines of the
journal [1] and submit their articles through the Editorial
Manager submission system [2]. <br>
When submitting the manuscript, select as article type
“VSI-Mach.Learn.Adv.Env.Sc”. <br>
[1]: <a class="moz-txt-link-freetext"
href="https://www.elsevier.com/journals/environmental-modelling-and-software/1364-8152/guide-for-authors"
moz-do-not-send="true">https://www.elsevier.com/journals/environmental-modelling-and-software/1364-8152/guide-for-authors</a>
<br>
[2]: <a class="moz-txt-link-freetext"
href="https://www.editorialmanager.com/envsoft/default.aspx"
moz-do-not-send="true">https://www.editorialmanager.com/envsoft/default.aspx</a>
<br>
<br>
========== <br>
Timetable <br>
========== <br>
<br>
01 Feb 2021 - Open for submissions <br>
01 July 2021 - ***Submission deadline*** <br>
July-August 2021 - Author notifications & revisions <br>
September 2021 - Final editorial decisions <br>
December 2021 - Publication <br>
<br>
================ <br>
Editor-in-Chief <br>
================ <br>
<br>
D.P. Ames, Brigham Young University, Provo, Utah, United States <br>
<br>
============== <br>
Guest Editors <br>
============== <br>
<br>
Ioannis N. Athanasiadis, Wageningen University and Research, The
Netherlands <br>
Francesco Camastra, University of Naples Parthenope, Italy <br>
Friedrich Recknagel, University of Adelaide, Australia <br>
Antonino Staiano, University of Naples Parthenope, Italy <br>
<br>
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