Multiscale Modelling of Daily Suspended Sediment Load Using MEMD-SLR Coupled Approach
Modelling suspended sediment load (SSL) from rivers is a complex problem in river basin management. This chapter presents hybrid framework multivariate empirical mode decomposition (MEMD) and stepwise linear regression (SLR) for estimation of SSL from riverflows demonstrated to a case study in Mahanadi River Basin, India. The method involves two major steps: first, the multivariate dataset comprising SSL of current time along with lagged inputs of streamflow and SSL are decomposed into different modes using MEMD; then, the obtained modes are estimated independently by SLR fitting engaging the statistically significant inputs at respective time scales. The sum of the predicted modes gives the desired SSL. The effectiveness of the presented method is evaluated for five models by considering different combinations of inputs, and their performance is compared with traditional multiple linear regression (MLR) and model tree (MT) models. The performance statistics of models showed that for estimation of SSL, the MEMD-SLR approach performs better than MLR and MT models.