This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessReview
A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques
by
Ghazi Al-Rawas
Ghazi Al-Rawas 1,*,
Mohammad Reza Nikoo
Mohammad Reza Nikoo 1
,
Malik Al-Wardy
Malik Al-Wardy 2
and
Talal Etri
Talal Etri 1
1
Department of Civil and Architectural Engineering, Sultan Qaboos University, 123 Muscat, Oman
2
Center for Environmental Studies and Research, Sultan Qaboos University, 123 Muscat, Oman
*
Author to whom correspondence should be addressed.
Water 2024, 16(14), 2069; https://doi.org/10.3390/w16142069 (registering DOI)
Submission received: 30 May 2024
/
Revised: 14 July 2024
/
Accepted: 17 July 2024
/
Published: 22 July 2024
(This article belongs to the Section
Hydrology)
Abstract
There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.
Share and Cite
MDPI and ACS Style
Al-Rawas, G.; Nikoo, M.R.; Al-Wardy, M.; Etri, T.
A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques. Water 2024, 16, 2069.
https://doi.org/10.3390/w16142069
AMA Style
Al-Rawas G, Nikoo MR, Al-Wardy M, Etri T.
A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques. Water. 2024; 16(14):2069.
https://doi.org/10.3390/w16142069
Chicago/Turabian Style
Al-Rawas, Ghazi, Mohammad Reza Nikoo, Malik Al-Wardy, and Talal Etri.
2024. "A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques" Water 16, no. 14: 2069.
https://doi.org/10.3390/w16142069
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.