doi: 10.7763/IJMO.2023.V13.838
Flood Prediction in the Lower Cape Fear River Using SAR Based Water Extraction
D. McMoran
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- 1 University of North Carolina Wilmington, Wilmington, North Carolina, USA.
- 2 University of North Carolina Wilmington (UNCW), where she founded the Applied AI Lab, USA
- Manuscript received November 23, 2022
- revised January 10, 2023
- accepted February 16, 2023
- published October 26, 2023
Abstract
The effects of climate change, including severe droughts, fires, and extreme weather events, are increasing every year. One of the most dangerous natural disasters around the world is flooding. Remote sensing and machine learning offer opportunities to utilize remotely collected data for analysis and predictive modeling purposes. Using Python programming methods, Sentinel-1 Synthetic Aperture Radar (SAR) data was collected for water pixel detection from Google Earth Engine and combined with National Oceanic and Atmospheric Administration (NOAA) precipitation data to understand seasonal flood events between 2015 and early 2022. Models were developed to predict flooding in the Lower Cape Fear and greater Wilmington, North Carolina area. One of the challenges in this method is the lack of imagery data recorded within the Region of Interest and time frame as well as the interplay between different data types, programming methods, and outputs. Outputs include data tables and multiple methods for data validation. Overall, accuracy for resulting models was high, with an artificial neural network for binary classification returning an accuracy value greater than 90%.
Keywords
- Flood detection
- machine learning
- predictive modeling
- SAR
How to Cite
D. McMoran, A. Langevin, A. Whittaker, P. Poosapati, N. Pricope, and G. Dogan, "Flood Prediction in the Lower Cape Fear River Using SAR Based Water Extraction," International Journal of Modeling and Optimization, vol. 13, no. 4, pp. 129-133, 2023. https://doi.org/ 10.7763/IJMO.2023.V13.838
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).