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\begin{document}

\title{Wavelet Neural Network and Long Short-Term Memory Recurrent Neural
Network for Rainfall and Runoff Prediction with Satellite-Derived
Meteorological Data}



\author[1]{Yashon Ouma}%
\affil[1]{Moi University}%


\vspace{-1em}



  
  \date{May 08, 2024}


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\begin{abstract}
At the catchment scales, the use of coupled, physically based and
spatially explicit modelling of hydrological processes continue to be
expensive due to the high computational costs and the demand for the
necessary meteorological input data. As such, physically based models
are still rarely used in operational rainfall--runoff forecasting. In
addition, for most regions, the required parameterization datasets for
the physical models, for example the 3D information on the physical
characteristics of the sub-surface, are not available. For operational
reasons therefore only simplified physical and conceptual models are
routinely used. To improve on the physical and conceptual models,
data-based mechanistic modelling concepts or fully data-driven
approaches including regression, fuzzy-based or artificial neural
networks (ANNs) have been developed and explored. This study evaluates
the performance of long short-term memory (LSTM) recurrent neural
networks (RNN) and the wavelet neural networks (WNN) for the prediction
of rainfall and runoff in data scarce basins. Both models were trained
and validated using 31-years (1979-2009) of observed data from three (3)
discharge stations, ten (10) rainfall stations with corresponding
satellite-derived meteorological data comprising of wind velocity,
relative humidity, average temperature and solar radiation. With four
(4) optimal hidden layers comprising of thirty (30) neurons each, the
best results for the runoff prediction using LSTM-RNN and WNN models
were determined with respective R2 of 0.8967 and 0.8820, with rainfall
as the most significant predicator variable. For rainfall prediction,
the LSTM-RNN outperformed WNN by 11\% as measured in terms of R2 and
through spatial distribution comparison with observed precipitation,
showing that the integration of satellite-observed meteorological data,
in the prediction of rainfall and runoff yields good results.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Manuscript_HYP_Ouma et al. Feb 2020.docx`  available at \url{https://authorea.com/users/297324/articles/426618-wavelet-neural-network-and-long-short-term-memory-recurrent-neural-network-for-rainfall-and-runoff-prediction-with-satellite-derived-meteorological-data}

\textbf{Hosted file}

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\end{document}

