A Review on Disaster Prediction Using Machine Learning - International Journal of Communication Networks 2024

Aug 2, 2026

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Climate change is intensifying the frequency and severity of natural disasters, including earthquakes, hurricanes, wildfires, and floods, leading to human casualties, major damage to infrastructure and property, and large-scale socioeconomic disruption. Existing efforts span early warning systems, risk assessment, disaster response and recovery, and predictive modelling. Recent advances in AI, deep learning, and machine learning can support disaster prediction, detection, mapping, evacuation, and relief using big data such as satellite imagery, social media, and GIS.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Disaster impact and trends

Role of disaster prediction and detection

Data sources and the need for advanced algorithms

Machine learning for disaster prediction (concept overview)

ML fundamentals and data-driven prediction

Deep learning for classification and detection

International Journal of Communication Networks 2024, 16(S1) ISSN: 2073-607X, 2076-0930 [https://](https://)[https://ijcnis.org/](https://ijcnis.org/ Research Article)[ Research Article](https://ijcnis.org/ Research Article) and Information Security | A Review on Disaster Prediction Using Machine Learning
Alaa Taiseer Farghaly1*, Ngahzaifa Binti Ab Ghani2, Abbas Saliimi Lokman3 1*Faculty of Computing, Centre for Artificial Intelligence & Data Science | | | --- | --- | | Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pahang, Malaysia Pahang, Malaysia
2Faculty of Computing, Centre for Artificial Intelligence & Data Science | | | Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pahang, Malaysia 3Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pahang, Malaysia
[Email:](Email:2zaifa@umpsa.edu.my)[2](Email:2zaifa@umpsa.edu.my)[zaifa@umpsa.edu.my](Email:2zaifa@umpsa.edu.my), [abbas@umpsa.edu.my](abbas@umpsa.edu.my)[ ](abbas@umpsa.edu.my)*Corresponding Author: Alaa Taiseer Farghaly Email:1*[alaataiseer22@gmail.com](alaataiseer22@gmail.com) | | | ARTICLE INFO ABSTRACT | | | Received: 09 May 2024
Accepted: 16 Sep 2024 | 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.
Keywords: Machine learning, Disaster prediction, Classification. Artificial Intelligence | Copyright © 2024 by Author/s and Licensed by IJCNIS. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. INTRODUCTION The world has witnessed a breaking number of disasters in the last few years with a record of more than 300 natural disaster in the second half of 2020 and the first of 2021. This outpacesthe recorded numbers from 2000-2019 with average of 185 disaster(Sreelakshmi and Vinod Chandra 2022) . A number of 389 climate-related disasters only were recorded during 2020. Asa result, the number of recorded disasters during the same year was greater than the average of the statistically recorder number in the previous years with 26% more storms and 23% more floods combined with a higher number of human and economic losses (Linardos et al. 2022a) . These calamitous incidents significantly affect the infrastructure and properties resulting in homeless people, impacts the public mental health of the survivors who have lost everything due to this unforeseen and unpredicted natural hazard in the affected area and consequently causes socioeconomical losses

FAQs

What problem does the review address regarding disasters and prediction?

The review addresses how rising natural disaster frequency and severity require better predictive capabilities. It focuses on research using machine learning to support forecasting and decision-making before, during, and after disasters.

Which technologies and data sources are highlighted for disaster prediction?

The document highlights AI, deep learning, and machine learning, supported by big data sources such as satellite imagery, social media, and geographical information systems (GIS).

What does the review conclude about future research and limitations?

It discusses limitations of current machine learning approaches for disaster prediction and classification and outlines future directions. It also emphasizes the need for further research in this area.

Aug 2, 2026

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