Abstract
Recently, surveillance technology was proposed as an alternative to flood monitoring systems. This study introduces a novel approach to flood monitoring by integrating surveillance technology and LiDAR data to estimate river water levels. The methodology involves deep learning semantic segmentation for water extent extraction before utilizing the segmented images and virtual markers with elevation information from light detection and ranging (LiDAR) data for water level estimation. The efficiency was assessed using Spearman's rank-order correlation coefficient, yielding a high correlation of 0.92 between the water level framework with readings from the sensors. The performance metrics were also carried out by comparing both measurements. The results imply accurate and precise model predictions, indicating that the model performs well in closely matching observed values. Additionally, the semi-automated procedure allows data recording in an Excel file, offering an alternative measure when traditional water level measurement is not available. The proposed method proves valuable for on-site water-related information retrieval during flood events, empowering authorities to make informed decisions in flood-related planning and management, thereby enhancing the flood monitoring system in Malaysia.
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Acknowledgements
This study was funded by Universiti Putra Malaysia under the Putra Grant, GP-IPM (Grant number: 9780100). Besides, the authors would like to appreciate the given opportunity to collaborate with the European Union under the RECONECT project funded by the European’s Union Horizon 2020 (Project ID: 776866). The authors also appreciate the support for this study from the Faculty of Engineering and the Institute of Aquaculture and Aquatic Sciences, Universiti Putra Malaysia. Lastly, the authors would like to thank the Department of Irrigation and Drainage for publicly sharing the surveillance images and water level data.
Open research
Input dataset are publicly available at the Zenodo data repository (Nur Atirah Muhadi 2022).
Funding
This study was funded by GP-IPM, Universiti Putra Malaysia (Grant number: 9780100). Besides, the authors would like to appreciate the given opportunity to collaborate with the European Union under the RECONECT project funded by the Europeans Union Horizon 2020 (Project ID: 776866).
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All authors contributed to the study conception and design. NAM and AFA have designed and conducted the research in collaboration with SKB and MRM, supervised by AM and ZV. NAM has written the manuscript with contributions from all co-authors.
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Appendices
Appendix A
The screenshot of the execution of the automated procedure for Kampung Selisek using the segmentation procedure of the GUI.
Appendix B
The segmented images were saved from the execution of the automated procedure for Kampung Selisek.
Appendix C
The result was from the water level estimation procedure in the GUI.
Appendix D
Image saved from the water level estimation procedure that includes the segmented result and water level status.
Appendix E
Excel sheet that contains the information of image submitted by the user in terms of locations, date, and time as well as the estimated water level values.
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Muhadi, N.A., Abdullah, A.F., Bejo, S.K. et al. Deep learning and LiDAR integration for surveillance camera-based river water level monitoring in flood applications. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06503-6
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DOI: https://doi.org/10.1007/s11069-024-06503-6