Issue |
E3S Web Conf.
Volume 314, 2021
The 6th edition of the International Conference on GIS and Applied Computing for Water Resources (WMAD21)
|
|
---|---|---|
Article Number | 02002 | |
Number of page(s) | 5 | |
Section | Big-data & Machine Learning | |
DOI | https://doi.org/10.1051/e3sconf/202131402002 | |
Published online | 26 October 2021 |
Proposal of a Big data System for an Intelligent Management of Water Resources
1
Informatics and Applications Laboratory, Science Faculty of Meknes, Moulay Ismail University, Meknes, Morocco
2
Ibn Tofail University, National School of Applied Sciences, Kenitra, Morocco
3
Laboratory of Geo-Engineering and Environment, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
4
Department of Environment, Functional ecology and environmental engineering laboratory, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco
* Corresponding author: sa.bouziane@edu.umi.ac.ma
Today, advanced technologies like Big Data, IoT, and Cloud Computing can provide new opportunities and applications in all sectors. In the water sector, water scarcity has become a common concern of different institutions and actors worldwide. In this context, several approaches and systems have been proposed and developed, using these technologies, allowing intelligent water resources management. Internet of Things can be used for assisting the Water Industry to collect data, manage and monitor the water infrastructures using smart devices. Big Data is a strategic technology for analyzing and interpreting collected data into valuable and helpful information for better decision making. This paper presents Big Data and Internet of Things technologies. It addresses theirs uses in some use cases such as municipal water losses, water pollution in agriculture, water Leak detection, etc., to provide new systems and innovative solutions for intelligent water resources management. Based on this study, we propose a Big Data and IoT architecture for intelligent water resources management.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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