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Machine learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia
  • Habtamu Tamiru
Habtamu Tamiru
Wollega University

Corresponding Author:habtamutami@wollegauniversity.edu.et

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This paper presents the integrated machine learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. A predictive rainfall-runoff and spatially distributed river simulation models were developed using Artificial Neural Networks (ANNs) and HEC-RAS respectively. Daily rainfall and temperature data of 7-yrs and Topographical Wetness Index (TWI) with a spatial resolution of 50 x 50m were used to train the ANN in R studio. The integration of the spatial and temporal variability in this paper improved the accuracy of the predictive models integrated with ANN and HEC-RAS. The predictive ANN model was tested with the observed daily discharge of the same temporal resolution and the rainfall-runoff result obtained from the tested ANN model was used as input for the HEC-RAS. The flood event of 2005 was used to verify the accuracy of flood generated in the HEC-RAS model by implementing the Normal Difference Water Index (NDWI). The comparison was made between the flood inundation map generated by HEC-RAS and flood events of different periods based on coverage percentage areas and a good agreement was reached with 96 % overlapped areas. The performance of ANN and HEC-RAS models were evaluated with 0.86 and 0.88 values at the training and testing period respectively. Finally, it was concluded that the integration of a machine learning approach with the HEC-RAS model in developing a flood inundation mapping is an appropriate tool to warn residents in this river basin.
06 Feb 2021Submitted to Hydrological Processes
08 Feb 2021Assigned to Editor
08 Feb 2021Submission Checks Completed
10 Feb 2021Reviewer(s) Assigned
10 Feb 2021Review(s) Completed, Editorial Evaluation Pending
Jun 2022Published in Modeling Earth Systems and Environment volume 8 issue 2 on pages 2291-2303. 10.1007/s40808-021-01175-8