Abstract
Floods are a complex phenomenon that are difficult to predict because of their non-linear and dynamic nature. Therefore, flood prediction has been a key research topic in the field of hydrology. Various researchers have approached this problem using different techniques ranging from physical models to image processing, but the accuracy and time steps are not sufficient for all applications. This study explores deep learning techniques for predicting gauge height and evaluating the associated uncertainty. Gauge height data for the Meramec River in Valley Park, Missouri was used to develop and validate the model. It was found that the deep learning model was more accurate than the physical and statistical models currently in use while providing information in 15 minute increments rather than six hour increments. It was also found that the use of data sub-selection for regularization in deep learning is preferred to dropout. These results make it possible to provide more accurate and timely flood prediction for a wide variety of applications, including transportation systems.
Recommended Citation
V. Gude et al., "Flood Prediction and Uncertainty Estimation using Deep Learning," Water, vol. 12, no. 3, article no. 884, MDPI AG, Mar 2020.
The definitive version is available at https://doi.org/10.3390/w12030884
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Second Research Center/Lab
Center for Research in Energy and Environment (CREE)
Third Research Center/Lab
Intelligent Systems Center
Keywords and Phrases
Deep learning; Flood; Gauge height prediction; Transportation; Uncertainty estimation
International Standard Serial Number (ISSN)
2073-4441; 2073-4441
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Mar 2020
Comments
This research was funded by the Missouri Department of Transportation; grant number TR201912 and the Mid-America Transportation Center, grant number 25-1121-0005-130.