A Rapid Assessment Method for Flood Risk Mapping Integrating Aerial Point Clouds and Deep Learning

Xin Fang, Jie Wu, Peiqi Jiang, Kang Liu, Xiaohua Wang (), Sherong Zhang, Chao Wang, Heng Li and Yishu Lai
Additional contact information
Xin Fang: Tianjin University
Jie Wu: Ministry of Water Resources
Peiqi Jiang: Tianjin University
Kang Liu: Tianjin University
Xiaohua Wang: Tianjin University
Sherong Zhang: Tianjin University
Chao Wang: Tianjin University
Heng Li: Hong Kong Polytechnic University
Yishu Lai: Tianjin University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 5, No 9, 1753-1772

Abstract: Abstract In recent years, floods have brought renewed attention and requirement for real-time and city-scaled flood forecasting due to climate change and urbanization. In this study, a rapid assessment method for flood risk mapping is proposed by integrating aerial point clouds and deep learning technique that is capable of superior modeling efficiency and analysis accuracy for flood risk mapping. The method includes four application modules, i.e., data acquisition and preprocessing by oblique photography, large-scale point clouds segmentation by RandLA-Net, high-precision digital elevation model (DEM) reconstruction by modified hierarchical smoothing filtering algorithm, and hydrodynamics simulation based on hydrodynamics. To demonstrate the advantages of the proposed rapid assessment method more clearly, a case study is conducted in a local area of the South-to-North Water Transfer Project in China. The proposed method achieved 70.85% in mean intersection over union (mIoU) and 88.70% in overall accuracy (OAcc), outperforming the PointNet and PointNet++ networks. For the case point cloud containing nearly 50 million points, the computation time is less than 9 min, while the computation times for PointNet and PointNet++ are both more than 24 h. Then, high-precision DEM reconstruction by proposed hierarchical smoothing method with topographic feature embedding. These results demonstrate the efficiency and accuracy of the proposed method in processing large-scale 3D point clouds and rapid assessment of flood risk, providing a new perspective and effective solution for flood risk mapping in the field of spatial information science.

Keywords: Flood risk mapping; Point clouds segmentation; DEM reconstruction; Hydrodynamics simulation (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-03764-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:5:d:10.1007_s11269-024-03764-5

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-03764-5

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().