Abstract:
Large-scale climatic circulation modulates the weather patterns around the world. Understanding the teleconnections between
large-scale circulation and local hydro-climatological variables has been a major thrust area of hydro-climatology research.
The large-scale circulation is often quantifed in terms of sea surface temperature (SST) and sea-level pressure (SLP). In this
paper, we investigate the potential of wavelet neural network (WNN) hybrid model to predict maximum monthly discharge
of the Madarsoo watershed, North of Iran considering two large-scale climatic signals like SST and SLP as inputs. Error
measures like root-mean-square error (RMSE), and mean absolute error along with the correlation measures like coefcient
of correlation (R), and Nash–Sutclife coefcient (CNS) were used to quantify the performance of prediction of maximum
monthly discharge of three diferent hydrometry stations of the watershed. In all the cases, the WNN hybrid machine learn ing model was found to be giving superior performance consistently against the standalone artifcial neural network (ANN)
model and multiple linear regression model to predict the food discharges of March and August months. The prediction
of food for August which is more devastating is found to be slightly better than the prediction of foods of March, in the
stations served with smaller drainage area. The RMSE, R and CNS of Tamer hydrometry station in August were found to
be 0.68, 0.996, and 0.99 m3
/s, respectively, for the test period by using WNN model against 1.55, 0.989 and 0.95 by ANN
model. Moreover, when evaluated for predicting the maximum monthly discharge in March and August between 2012 and
2013, the wavelet-based neural networks performed remarkably well than the ANN
