Mode decomposition based large margin distribution machines for sediment load prediction

Abstract

Precise suspended sediment load (SSL) prediction is essential for irrigation, hydropower and river management practices. But due to some external factors such as high altitude, heavy monsoon and tropical climate conditions, the data we collect may contain noisy samples. Hence, it becomes a challenging task to accurately predict the SSL in rivers. Therefore, to diminish the influence of noise, the empirical mode decomposition (EMD) -based techniques can be adopted. Moreover, the large margin distribution machine-based regression (LDMR) can deal efficiently with noisy datasets as it simultaneously minimizes the insensitive loss as well as quadratic loss. The significance of this work lies in its contribution to improving the accuracy of suspended sediment load (SSL) prediction in rivers, which has practical implications for various applications such as irrigation, hydropower, and river management. We recognize the challenges posed by external factors, which introduce noise into the collected data, making accurate prediction of SSL difficult. Overall, the significance of this work lies in its novel integration of EMD-based techniques and LDMR-based models, which address the challenges posed by noisy and non-stationary sediment load data. The findings contribute to the field of SSL prediction, offering practical solutions for managing and utilizing river resources more effectively in various domains. The main advantage of the least squares LDMR (LS-LDMR) approach is that it solves a system of linear equations rather than solving a large QPPs unlike SVR, TSVR, LDMR, which makes it computationally efficient. It is well known that the sediment load data is complex and non-stationary in nature. Therefore, for daily SSL prediction, the LDMR and least squares LDMR (LSLDMR) models are embedded with two different decomposition techniques, EMD and ensemble EMD (EEMD), to handle the nonlinear and non-stationary characteristics of sediment load data on the forecasted outputs and to increase prediction ability. The results of the proposed EMD-LDMR, EEMD-LDMR, EMD-LSLDMR and EEMD-LSLDMR are compared with the conventional support vector regression (SVR), twin SVR (TSVR), LDMR and LSLDMR. The performance of the evaluated using the best-fit model using the mean absolute error (MAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), Willmott’s index (WI), correlation coefficient (CC) and R2. Better or comparable results specify the efficiency of the suggested models. For graphical visualization, scatterplots, prediction error plots and violin plots are shown. Numerical results show that these hybrid models show excellent prediction performance. The overall analysis of the results suggests using EEMD-LSLDMR for SSL estimation.