An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelli...

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Autores principales: Boulouard, Zakaria, Ouaissa, Mariyam, Ouaissa, Mariya, Siddiqui, Farhan, Almutiq, Mutiq, Krichen, Moez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740278/
https://www.ncbi.nlm.nih.gov/pubmed/36502187
http://dx.doi.org/10.3390/s22239485
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author Boulouard, Zakaria
Ouaissa, Mariyam
Ouaissa, Mariya
Siddiqui, Farhan
Almutiq, Mutiq
Krichen, Moez
author_facet Boulouard, Zakaria
Ouaissa, Mariyam
Ouaissa, Mariya
Siddiqui, Farhan
Almutiq, Mutiq
Krichen, Moez
author_sort Boulouard, Zakaria
collection PubMed
description River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.
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spelling pubmed-97402782022-12-11 An Integrated Artificial Intelligence of Things Environment for River Flood Prevention Boulouard, Zakaria Ouaissa, Mariyam Ouaissa, Mariya Siddiqui, Farhan Almutiq, Mutiq Krichen, Moez Sensors (Basel) Article River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions. MDPI 2022-12-05 /pmc/articles/PMC9740278/ /pubmed/36502187 http://dx.doi.org/10.3390/s22239485 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Boulouard, Zakaria
Ouaissa, Mariyam
Ouaissa, Mariya
Siddiqui, Farhan
Almutiq, Mutiq
Krichen, Moez
An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
title An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
title_full An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
title_fullStr An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
title_full_unstemmed An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
title_short An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
title_sort integrated artificial intelligence of things environment for river flood prevention
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740278/
https://www.ncbi.nlm.nih.gov/pubmed/36502187
http://dx.doi.org/10.3390/s22239485
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