Remote Sensing Methods for Flood Prediction: A Review

Article


Munawar, Hafiz Suliman, Hammad, Ahmed W. A. and Waller, S. Travis. 2022. "Remote Sensing Methods for Flood Prediction: A Review." Sensors. 22 (3), pp. 1-21. https://doi.org/10.3390/s22030960
Article Title

Remote Sensing Methods for Flood Prediction: A Review

ERA Journal ID34304
Article CategoryArticle
AuthorsMunawar, Hafiz Suliman, Hammad, Ahmed W. A. and Waller, S. Travis
Journal TitleSensors
Journal Citation22 (3), pp. 1-21
Article Number960
Number of Pages21
Year2022
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s22030960
Web Address (URL)https://www.mdpi.com/1424-8220/22/3/960
Abstract

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.

KeywordsDisaster management; Flood forecasting; Flood hazard assessment; Flood prediction; Flood risk analysis; Remote sensing
Byline AffiliationsUniversity of New South Wales
Library Services
Permalink -

https://research.usq.edu.au/item/w7562/remote-sensing-methods-for-flood-prediction-a-review

Download files


Published Version
sensors-22-00960.pdf
License: CC BY 4.0
File access level: Anyone

  • 48
    total views
  • 14
    total downloads
  • 3
    views this month
  • 1
    downloads this month

Export as

Related outputs

Towards 6G Internet of Things: Recent advances, use cases, and open challenges
Qadir, Zakria, Le, Khoa N., Saeed, N. and Munawar, Hafiz Suliman. 2023. "Towards 6G Internet of Things: Recent advances, use cases, and open challenges." ICT Express. 9 (3), pp. 296-312. https://doi.org/10.1016/j.icte.2022.06.006
An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective
Munawar, Hafiz Suliman, Mojtahedi, Mohammad, Hammad, Ahmed W. A., Ostwald, Michael J. and Waller, S. Travis. 2022. "An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective." Buildings. 12 (1), pp. 1-23. https://doi.org/10.3390/buildings12010080
Disruptive technologies as a solution for disaster risk management: A review
Munawar, Hafiz Suliman, Mojtahedi, Mohammad, Hammad, Ahmed W.A., Kouzani, Abbas and Mahmud, M. A. Parvez. 2022. "Disruptive technologies as a solution for disaster risk management: A review." Science of the Total Environment. 806 (Part 3). https://doi.org/10.1016/j.scitotenv.2021.151351
Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks
Akram, Junaid, Munawar, Hafiz Suliman, Kouzani, Abbas Z. and Mahmud, M. A. Parvez. 2022. "Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks." Sensors. 22 (3), pp. 1-23. https://doi.org/10.3390/s22031083
Automatic Target Detection from Satellite Imagery Using Machine Learning
Tahir, Arsalan, Munawar, Hafiz Suliman, Akram, Junaid, Adil, Muhammad, Ali, Shehryar, Kouzani, Abbas Z. and Mahmud, M. A. Parvez. 2022. "Automatic Target Detection from Satellite Imagery Using Machine Learning." Sensors. 22 (3), pp. 1-22. https://doi.org/10.3390/s22031147
Disaster Region Coverage Using Drones: Maximum Area Coverage and Minimum Resource Utilisation
Munawar, Hafiz Suliman, Hammad, Ahmed W.A. and Waller, S. Travis. 2022. "Disaster Region Coverage Using Drones: Maximum Area Coverage and Minimum Resource Utilisation." Drones. 6 (4), pp. 1-28. https://doi.org/10.3390/drones6040096
Insights into the Mobility Pattern of Australians during COVID-19
Munawar, Hafiz Suliman, Khan, Sara Imran, Qadir, Zakria, Kiani, Yusra Sajid, Kouzani, Abbas Z. and Mahmud, M. A. Parvez. 2021. "Insights into the Mobility Pattern of Australians during COVID-19." Sustainability. 13 (17), pp. 1-19. https://doi.org/10.3390/su13179611
A prototype of an energy-efficient MAGLEV train: A step towards cleaner train transport
Qadir, Zakria, Munir, Arslan, Ashfaq, Tehreem, Munawar, Hafiz Suliman, Khan, Muazzam A. and Le, Khoa. 2021. "A prototype of an energy-efficient MAGLEV train: A step towards cleaner train transport." Cleaner Engineering and Technology. 4, pp. 1-11. https://doi.org/10.1016/j.clet.2021.100217
Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids
Qadir, Zakria, Khan, Sara Imran, Khalaji, Erfan, Munawar, Hafiz Suliman, Al-Turjman, Fadi, Mahmud, M. A. Parvez, Kouzani, Abbas Z. and Le, Khoa. 2021. "Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids." Energy Reports. 7, pp. 8465-8475. https://doi.org/10.1016/j.egyr.2021.01.018
A review on flood management technologies related to image processing and machine learning
Munawar, Hafiz Suliman, Hammad, Ahmed W. A. and Waller, S. Travis. 2021. "A review on flood management technologies related to image processing and machine learning." Automation in Construction. 132, pp. 1-18. https://doi.org/10.1016/j.autcon.2021.103916
Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation
Akram, Junaid, Tahir, Arsalan, Munawar, Hafiz Suliman, Akram, Awais, Kouzani, Abbas Z. and Mahmud, M. A. Parvez. 2021. "Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation." Sensors. 21 (23), pp. 1-22. https://doi.org/10.3390/s21237846
Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions
Iqbal, Kamran, Munawar, Hafiz Suliman, Inam, Hina and Qayyum, Siddra. 2021. "Promoting Customer Loyalty and Satisfaction in Financial Institutions through Technology Integration: The Roles of Service Quality, Awareness, and Perceptions." Sustainability. 13 (23), pp. 1-20. https://doi.org/10.3390/su132312951