Assessing Combinations of Artificial Neural Networks Input Meteorological Parameters to Improve Daily Runoff Simulation
No Thumbnail Available
Date
2022-01-24
Journal Title
Journal ISSN
Volume Title
Publisher
University of Eloued جامعة الوادي
Abstract
Hydrological models are one of the tools used for reconstruction or
simulation, forecasting for the anticipation of future changes in the flow of a
river, which allows better management of water resources during low flow
periods and anticipation of flood risks during high water periods. Using
artificial neural network (ANN) in the fields of hydrology and water resources,
became advocated due to its capability of tackling, modeling and forecasting
the problems that are nonlinear or stochastic within the Rainfall-Runoff (R-R)
system. This research aims to test the practicability of using ANNs with two
input configurations; to model the R-R relationship in two stations Mirebek and
Ain Berda located in the Seybouse basin in Algeria. The 1st (ANN IP)
considers only precipitation as an input variable for the daily flow simulation.
The 2nd (ANN IIP,T,Hu) considers a combination of the Temperature and
Humidity with precipitation at the model input. The results of the two models
were compared through performance metrics, viz., Root Mean Square Error
(RMSE),mean absolute error (MAE), Pearson's correlation coefficient (R),
Nash Sutcliffe Efficiency coefficient (NSE), and through graphical
interpretation (scatter plots and time series). Better flow simulations were
provided by the three-input model for the two stations; where R=0.90,
NSE=80.5% for Mirebek station and R=0.85, NSE=72% for Ain Berda. This
result has confirmed that as much input variables are numerous, as more the
model of ANN is efficient. The finding of this study indicates that the
developed ANN models could be considered as a powerful tool for predicting
Runoff.
Description
Forum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloued
Keywords
ANN, Predicting runoff, Seybouse basin, Meteorological parameters.
Citation
Aoulmi, Yamina. Marouf , Nadir. Amireche, Mohamed. Assessing Combinations of Artificial Neural Networks Input Meteorological Parameters to Improve Daily Runoff Simulation. Forum of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here]