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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9740278 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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