Multiscale modelling of monthly streamflows using MEMD-GP coupled approach. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Adarsh, S.; Janga Reddy, M.
• Is Part Of: International journal of river basin management. Volume 18:Issue 2(2020)
• Page Start: 139
• Page End: 151
• Publication Date: 2020-04-02
• Subjects: Streamflow -- correlation -- multiscale -- MEMD -- TDIC; Watershed management -- Periodicals; Water resources development -- Periodicals; Hydraulic engineering -- Periodicals; Watershed hydrology -- Periodicals; Water resources development; Watershed management; Watersheds; Periodicals; 551.483
• DOI: 10.1080/15715124.2019.1656220
• Languages: English
• ISSNs: 1814-2060

Abstract

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.