Scientific Association for Water Information & Sustainability (SAWIS) and National School of Applied Sciences of Kenitra (ENSA KENITRA), organize the 6th edition of SAWIS International Conference on Water Management, Applied Computing and Data Science (WMAD), will be held at ENSA Kenitra on 24-25 September 2021. WMAD2021 will provide an opportunity for researchers, academic faculty, students, …
WMAD2021 is in forthcoming Proceeding of E3S Web Conference published here
After selection of the best papers by the committees, the authors will be invited to send their extended version to participate in Book Chapter
If you have any questions or concerns, please feel free to contact us at:firstname.lastname@example.org
Publication notice and Review process :
All submissions should be original, professional and have not been published elsewhere. Paper length should exceed 3 pages followed (Max 6 pages) and it need be formatted strictly according to the Template.
Submissions must be original, unpublished work, and not have been submitted to another conference or journal for publication. All submission will be peer-reviewed roughly by at least 2-3 experts.
Authors are invited to submit English papers. Please confirm your papers with clear argumentation, close core, sufficient theoretical analysis, proper language and standard grammar in English.
Submission of a paper implies that should the paper be accepted for formal publication, at least one of the authors will register and present the paper in the conference. Plagiarism in any form is not allowed.
WMAD’21 uses the i-Thenticate software to detect instances of overlapping and similar text in submitted manuscripts. i-Thenticate software checks content against a database of periodicals, the Internet, and a comprehensive article database.
As part of WMAD, the organizes include a set workshops and training courses about use of ICT for water resources management.
The topic of those workshops :
Open Source GIS for water resources, Remote sensing for water resources, Modelling for water resources, Data mining for water resources, Machine learning for water resources.