Evidence map›Paper›PMID 36502187›Full record

ArticleSensors (Basel, Switzerland)2022

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention.

Zakaria Boulouard, Mariyam Ouaissa, Mariya Ouaissa, Farhan Siddiqui, Mutiq Almutiq, Moez Krichen

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zakaria BoulouardLIM, Hassan II University of Casablanca, Casablanca 20000, Morocco.ORCID 0000-0002-4891-3760
Mariyam OuaissaDepartment of Computer Science, Moulay Ismail University, Meknes 50050, Morocco.ORCID 0000-0002-3993-8405
Mariya OuaissaDepartment of Computer Science, Moulay Ismail University, Meknes 50050, Morocco.ORCID 0000-0002-0088-3742
Farhan SiddiquiData Science Department, NED University of Engineering and Technology, Karachi 75270, Pakistan.
Mutiq AlmutiqDepartment of Management Information Systems and Production Management, College of Business and Economics, Qassim University, P.O. Box 6640, Buraidah 51452, Saudi Arabia.ORCID 0000-0002-4611-6493
Moez KrichenFCSIT, Al-Baha University, Al-Baha 65528, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceDisastersEnvironment, ControlledFloodsHumansRiversartificial intelligenceartificial intelligence of thingsinternet of thingslorawanriver flood predictionsensor nodes

Identifiers

PMID36502187
PMCPMC9740278

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.