Scholarly record
APPLICATION OF DRONE TECHNOLOGY FOR FLOOD RISK MONITORING AND MODELING
Abstract
The increasing frequency and intensity of flood events necessitate innovative approaches for effective monitoring and modeling to mitigate risks. This article explores the application of drone technology in flood risk management, highlighting its advantages over traditional methods. Drones equipped with high-resolution cameras and advanced sensors can rapidly collect spatial data, enabling detailed topographic assessments and hydrological modeling. Their ability to access hard-to-reach areas allows for real-time monitoring of flood-prone regions and infrastructure, improving response times during emergency situations. Case studies illustrate the successful use of drones in flood risk assessment, mapping, and data validation, demonstrating their potential to enhance decision-making for urban planning and disaster preparedness. Drone applications in flood management encompass a range of functionalities that enhance monitoring, modeling and response strategies. Drones helps and use for Data collection and mapping; Real time monitoring; Risk assessment; Damage assessment - this rapid assessment supports emergency response efforts and aids in recovery planning; Enviromental monitoring; Drones could integrate with other technologies - Geographical information systems (GIS) and data analytics tools to enhance flood modeling and prediction capabilities. The integration of aerial imagery and remote sensing data into flood models underscores the transformative role of drone technology in building resilient communities against flooding. This article emphasizes the need for further research and collaboration across disciplines to optimize drone applications in flood risk management. The aim of the research is Examine modern drone technologies and their application in flood risk monitoring; The adoption of drone technology in flood management provides a cost - effective, efficient, and innovative approach that significantly enchances preparednes and resilience against flooding events. To fulfill the research aim, certain objectives must be completed: 1. Evaluate currently available drone sensors and their use in data collection for flood modeling; 2. Assess the processes involved in modern flood risk monitoring and modeling; 3. To analyse the area of Latvia that are currently most exposed to flood risk and to assess what are the key conditions that contribute to them; 4. Evaluate the integration of drone technologies and their data into a modern flood monitoring and modeling system.
Publication Impact Profile
- Captures
- Mendeley - Readers: 14
Publication details
References14
Salmoral, G., Rivas Casado, M., Muthusamy, M., Butler, D., Menon, P. P., & Leinster, P. (2020). Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response. Water, 12(2), 521. DOI: 10.3390/w12020521;
Zhonghan Chen, (2024). Application of UAV remote sensing in natural disaster monitoring and early warning: an example of flood and mudslide and earthquake disasters. Highlights in Science, Engineering and Technology, Volume 85, 10p; DOI: 10.54097/zak5hp77
H. R. Spero, I. Vazquez-Lopez, K. Miller, R. Joshaghani, S. Cutchin & J. Enterkine (2022) Drones, virtual reality, and modeling: communicating catastrophic dam failure, International Journal of Digital Earth, 15:1, 585-605, DOI: 10.1080/17538947.2022.2041116;
Iqbal, U.; Riaz, M.Z.B.; Zhao, J.; Barthelemy, J.; Perez, P. Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review. Drones 2023, 7, 32. https://doi.org/10.3390/ drones7010032; DOI: 10.3390/drones7010032
Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui, Karim I. Abdrabo, Sameh A. Kantoush, Tetsuya Sumi, Hamouda Boutaghane, Tomoharu Hori, Doan Van Binh, Binh Quang Nguyen, Thao T. P. Bui, Ngoc Duong Vo, Emad Habib & Emad Mabrouk (2023) Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling, Geomatics, Natural Hazards and Risk, 14:1, 2203798, DOI: 10.1080/19475705.2023.2203798;
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Saman Razavi & Soo-Mi Choi (2024) Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling, International Journal of Digital Earth, 17:1, 2311325, DOI: 10.1080/17538947.2024.2311325;
Woo H-J, Seo D-M, Kim M-S and Lee H-K (2024) Development of methodology for calculating flooded area and flood volume in small urban areas based on unmanned aerial vehicle images. Front. Built Environ. 10:1482330. DOI: 10.3389/fbuil.2024.1482330;
Backes, D., Schumann, G., Teferle, F. N., and Boehm, J, (2019). TOWARDS A HIGH-RESOLUTION DRONE-BASED 3D MAPPING DATASET TO OPTIMISE FLOOD HAZARD MODELLING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 181�187, DOI: 10.5194/isprs-archives-XLII-2-W13-181-2019;
Emilia Karamuz, Renata J. Romanowicz, Joanna Doroszkiewicz. (2019), The use of unmanned aerial vehicles in flood hazard assessment, Chartered Institution of Water and Environmental Management, Journal of Flood Risk Management Wiley, 12pp, DOI: 10.1111/jfr3.12622;
Trepekli, K., Balstrom, T., Friborg, T. et al. UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment. Nat Hazards 113, 423�451 (2022). DOI: 10.1007/s11069-022-05308-9;
Clasing, R.; Munoz, E.; Arumi, J.L.; Parra, V. Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records. Water 2023, 15, 3813. https:// doi.org/DOI: 10.3390/w15213813;
Rizk, H., Nishimur, Y., Yamaguchi, H., & Higashino, T. (2022). Drone-Based Water Level Detection in Flood Disasters. International Journal of Environmental Research and Public Health, 19(1), 237. DOI: 10.3390/ijerph19010237;
Flood Map Modelling calculator; Available online: https://www.floodmap.net/?gi=459283;
Farhan Aziz, Xiuquan Wang, Muhammad Qasim Mahmood, Muhammad Awais, Bill Trenouth, Coastal urban flood risk management: Challenges and opportunities ? A systematic review, Journal of Hydrology, Volume 645, Part B, 2024, 132271, ISSN 0022-1694, DOI: 10.1016/j.jhydrol.2024.132271;
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.