Abstract:
This study presented multiscale characterization of monthly streamflow time series using Multivariate
Empirical Mode Decomposition (MEMD) and developed an innovative approach for streamflow
prediction by coupling MEMD with Genetic Programming (GP). Firstly, the possible hydro-climatic
teleconnection of monthly streamflows of Mahanadi river basin in India with two large-scale
climate oscillations of ElNiño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation
(EQUINOO) is investigated by applying MEMD based Time-Dependent Intrinsic Correlation (TDIC)
analysis. The TDIC analysis showed that the association between large-scale climate oscillations and
streamflows is not unique always, but both the nature and strength of the association varies with
time scales and over the time domain. Based on this finding, the study proposed MEMD-GP
coupled approach for streamflow prediction, in which different modes corresponding to different
process scales obtained by the MEMD are predicted separately using GP; and summation of these
predicted modes provides the monthly streamflow at the station. A statistical performance
evaluation based on multiple criteria showed that the proposed approach performs better than the
multiple linear regression, M5 model tree and GP models for monthly streamflow prediction
including extreme low and high flows, due to its unique capability to include the significant
predictors at different time scales.
