THENDIYATH ROSHNI
Articles written in Journal of Earth System Science
Volume 130 All articles Published: 10 February 2021 Article ID 0035 Research article
BEERAM SATYA NARAYANA REDDY S K PRAMADA THENDIYATH ROSHNI
Accurate surface runoff prediction is vital for water resources engineers for various applications. Advances in the artificial intelligence techniques can act as robust tools for modelling hydrological processes. The present study focuses on testing the reliability of different data sources and choosing the correct source to model the rainfall-runoff process under data scarce situations using AI techniques. In this study, an absolute homogeneity test was performed for TRMM, gridded and observed precipitation data and found that the observed precipitation dataset is homogeneous and best suitable for modelling rainfall-runoff process in Kallada river basin, Kerala. Emotional artificial neural network (EANN) is a novel hybrid neural network and it is suggested in the present study for accurate monthly surface runoff prediction. This study was also conceived to address and investigate the efficiency of EANN for forecasting monthly surface runoff and compare the performances with conventional feed forward neural network (FFNN) and multivariate adaptive regression spline (MARS) models. Suitable goodness-of-fit criteria such as Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and coefficient of determination (
$\bf{Highlights}$
$\bullet$ The present study focuses on testing the reliability of different data sources such as gridded, observed and TRMM precipitation datasets and choosing the correct source to model the rainfall-runoff process using AI techniques.
$\bullet$ From the selected homogeneous dataset and the observed runoff data, potential predictors were identified based on correlation analysis and partial autocorrelation function (PACF).
$\bullet$ The monthly runoff prediction models were developed using three AI techniques namely FFNN, MARS and EANN in a tropical river basin (Kallada) of Kerala with scarce amount of data.
$\bullet$ The performance of the developed models were assessed using statistical indicators (NSE, RMSE, and $R^{2}$) and graphical indicators (Taylor diagram, REC plots and Random walk test).
Volume 132, 2023
All articles
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