Internet of Things for Hydrology: Potential and Challenges
Zanella, Andrea; Zubelzu, Sergio; Bennis, Mehdi; Capuzzo, Martina; Tarolli, Paolo (2023-03-13)
IEEE
13.03.2023
A. Zanella, S. Zubelzu, M. Bennis, M. Capuzzo and P. Tarolli, "Internet of Things for Hydrology: Potential and Challenges," 2023 18th Wireless On-Demand Network Systems and Services Conference (WONS), Madonna di Campiglio, Italy, 2023, pp. 114-121, doi: 10.23919/WONS57325.2023.10062193
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502061479
https://urn.fi/URN:NBN:fi:oulu-202502061479
Tiivistelmä
Abstract
The management of water resources has always been important for the sustainability of our society and economy. This need has been further increased by climate change in recent years that, among other effects, has led to an increase in extreme events, such as prolonged droughts, severe storms, hurricanes, and so on. It is therefore urgent and critical to develop new and more sophisticated tools and methodologies to observe and possibly predict fundamental water processes. Internet of Things and machine learning can provide a significant contribution to this end, which requires bridging the gap that still exists between the communities of hydrologists, data scientists, and communications engineers. This article aims to help fill such a gap by introducing engineers to the challenges of hydrology, and reviewing existing solutions proposed in the literature to such challenges. Some results obtained from empirical data sets are used to illustrate the main concepts and corroborate the theoretical discussion with some practical examples. Finally, open problems and possible avenues for future research are discussed.
The management of water resources has always been important for the sustainability of our society and economy. This need has been further increased by climate change in recent years that, among other effects, has led to an increase in extreme events, such as prolonged droughts, severe storms, hurricanes, and so on. It is therefore urgent and critical to develop new and more sophisticated tools and methodologies to observe and possibly predict fundamental water processes. Internet of Things and machine learning can provide a significant contribution to this end, which requires bridging the gap that still exists between the communities of hydrologists, data scientists, and communications engineers. This article aims to help fill such a gap by introducing engineers to the challenges of hydrology, and reviewing existing solutions proposed in the literature to such challenges. Some results obtained from empirical data sets are used to illustrate the main concepts and corroborate the theoretical discussion with some practical examples. Finally, open problems and possible avenues for future research are discussed.
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