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\begin{document}

\title{Machine learning and HEC-RAS integrated models for flood inundation
mapping in Baro River Basin, Ethiopia}



\author[1]{Habtamu Tamiru}%
\affil[1]{Wollega University}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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