Inundation mapping is a critical task for damage assessment, emergency management, and prioritizing relief efforts during a flooding event. Remote sensing has been an effective tool for interpreting and analyzing water bodies and detecting floods over the past decades. In recent years, deep learning algorithms such as convolutional neural networks (CNNs) have demonstrated promising performance for remote sensing image classification for many applications, including inundation mapping. Unlike conventional algorithms, deep learning can learn features automatically from large datasets. This research aims to compare and investigate the performance of two state-of-the-art methods for 3D inundation mapping: a deep learning-based image analysis and a Geomorphic Flood Index (GFI). The first method, deep learning image analysis involves three steps: 1) image classification to delineate flood boundaries, 2) integrate the flood boundaries and topography data to create a three-dimensional (3D) water surface, and 3) compare the 3D water surface with pre-flood topography to estimate floodwater depth. The second method, i.e., GFI, involves three phases: 1) calculate a river basin morphological information, such as river height (hr) and elevation difference (H), 2) calibrate and measure GFI to delineate flood boundaries, and 3) calculate the coefficient parameter ( α ), and correct the value of hr to estimate inundation depth. The methods were implemented to generate 3D inundation maps over Princeville, North Carolina, United States during hurricane Matthew in 2016. The deep learning method demonstrated better performance with a root mean square error (RMSE) of 0.26 m for water depth. It also achieved about 98% in delineating the flood boundaries using UAV imagery. This approach is efficient in extracting and creating a 3D flood extent map at a different scale to support emergency response and recovery activities during a flood event.
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The need for paleoflood investigations on the American reach of the Red River of the North
Over the past century, the Red River of the North has been the least stationary river in the continental United States. In Canada, historical and paleoenvironmental evidence indicates severe floods were common during the early 1800s, with the record ce 1826 flood having an estimated peak discharge 50% higher than the second-most severe flood ever observed. Unfortunately, the recorded history of flooding upstream in the United States does not begin until seven decades after this event. If 1826 was an equally exceptional flood on American reach of the river, then current flood-frequency curves for the river underestimate significantly the risks posed by future flooding. Alternatively, if the American stretch did not produce a major flood in 1826, then the recent spate of flooding that has occurred over the past two decades is exceptional within the context of the past 200 years. Communities in the Fargo-Moorhead metropolitan area are building a 58-km long, $2.75 billion (USD) diversion channel that would redirect floodwaters westward around the two cities before returning it to the main channel. Because this and other infrastructure in North Dakota and Minnesota is intended to provide protection against low-probability, high-magnitude floods, new paleoflood investigations in the region would help local, state, and federal policy-makers better understand the true flood threats posed by the Red River of the North.
more » « less- NSF-PAR ID:
- 10362942
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- The Holocene
- Volume:
- 32
- Issue:
- 3
- ISSN:
- 0959-6836
- Page Range / eLocation ID:
- p. 220-225
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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