Cover Image

A review on disaster prediction using machine learning

Climate changes are increasing, with it the natural disasters such as earthquakes, hurricanes forest fire, and floods occurrence rate are also on the rise. These devastating incidents result in human losses, significant impacts on infrastructure and properties and often catastrophic socioeconomic im...

Full description

Saved in:
Bibliographic Details
Main Authors: Farghaly, Alaa Taiseer, Ngahzaifa, Ab Ghani, Abbas Saliimi, Lokman
Format: Article
Language:English
Published: Institute of Information Technology, Kohat University of Science and Technology 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/42622/1/A%20review%20on%20disaster%20prediction%20using%20machine%20learning.pdf
http://umpir.ump.edu.my/id/eprint/42622/
https://www.ijcnis.org/index.php/ijcnis/article/view/7088
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.42622
record_format eprints
spelling my.ump.umpir.426222024-09-20T03:38:38Z http://umpir.ump.edu.my/id/eprint/42622/ A review on disaster prediction using machine learning Farghaly, Alaa Taiseer Ngahzaifa, Ab Ghani Abbas Saliimi, Lokman QA75 Electronic computers. Computer science Climate changes are increasing, with it the natural disasters such as earthquakes, hurricanes forest fire, and floods occurrence rate are also on the rise. These devastating incidents result in human losses, significant impacts on infrastructure and properties and often catastrophic socioeconomic impacts. A lot of approaches have been taken to address issues related to natural disasters i.e. the development of early warning systems, risk assessment and management, disaster response and recovery, and the modelling of the natural disasters for the purposes of prediction and forecasting. The recent development in artificial intelligence (AI), deep learning (DL) and machine learning (ML) can help in better cope with the disaster prediction, detection, mapping, evacuation, and relief activities using sources of big data such as satellite imagery, social media, and geographical information systems (GIS). This paper aims to review research studies that utilize big and complex datasets to develop ML system that can predict and assist before, during and after disasters. Finally, the paper discusses the limitations and future directions of using machine learning for disaster prediction, classification, and highlights the need for further research in this area. Overall, this paper provides a comprehensive overview of the current state of the art in using machine learning for disaster prediction, classification and identifies opportunities for future research. Institute of Information Technology, Kohat University of Science and Technology 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42622/1/A%20review%20on%20disaster%20prediction%20using%20machine%20learning.pdf Farghaly, Alaa Taiseer and Ngahzaifa, Ab Ghani and Abbas Saliimi, Lokman (2024) A review on disaster prediction using machine learning. International Journal of Communication Networks and Information Security (IJCNIS), 16 (1 (Special Issue)). pp. 1402-1415. ISSN 2073-607X. (Published) https://www.ijcnis.org/index.php/ijcnis/article/view/7088
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Farghaly, Alaa Taiseer
Ngahzaifa, Ab Ghani
Abbas Saliimi, Lokman
A review on disaster prediction using machine learning
description Climate changes are increasing, with it the natural disasters such as earthquakes, hurricanes forest fire, and floods occurrence rate are also on the rise. These devastating incidents result in human losses, significant impacts on infrastructure and properties and often catastrophic socioeconomic impacts. A lot of approaches have been taken to address issues related to natural disasters i.e. the development of early warning systems, risk assessment and management, disaster response and recovery, and the modelling of the natural disasters for the purposes of prediction and forecasting. The recent development in artificial intelligence (AI), deep learning (DL) and machine learning (ML) can help in better cope with the disaster prediction, detection, mapping, evacuation, and relief activities using sources of big data such as satellite imagery, social media, and geographical information systems (GIS). This paper aims to review research studies that utilize big and complex datasets to develop ML system that can predict and assist before, during and after disasters. Finally, the paper discusses the limitations and future directions of using machine learning for disaster prediction, classification, and highlights the need for further research in this area. Overall, this paper provides a comprehensive overview of the current state of the art in using machine learning for disaster prediction, classification and identifies opportunities for future research.
format Article
author Farghaly, Alaa Taiseer
Ngahzaifa, Ab Ghani
Abbas Saliimi, Lokman
author_facet Farghaly, Alaa Taiseer
Ngahzaifa, Ab Ghani
Abbas Saliimi, Lokman
author_sort Farghaly, Alaa Taiseer
title A review on disaster prediction using machine learning
title_short A review on disaster prediction using machine learning
title_full A review on disaster prediction using machine learning
title_fullStr A review on disaster prediction using machine learning
title_full_unstemmed A review on disaster prediction using machine learning
title_sort review on disaster prediction using machine learning
publisher Institute of Information Technology, Kohat University of Science and Technology
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42622/1/A%20review%20on%20disaster%20prediction%20using%20machine%20learning.pdf
http://umpir.ump.edu.my/id/eprint/42622/
https://www.ijcnis.org/index.php/ijcnis/article/view/7088
_version_ 1822924666937802752
score 13.235362

Similar Items