Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model

Abstract

River inflow prediction plays an important role in water resources management and power-generating systems. But the noises and multi-scale nature of river inflow data adds an extra layer of complexity towards accurate predictive model. To overcome this issue, we proposed a hybrid model, Variational Mode Decomposition (VMD), based on a singular spectrum analysis (SSA) denoising technique. First, SSA his applied to denoise the river inflow data. Second, VMD, a signal processing technique, is employed to decompose the denoised river inflow data into multiple intrinsic mode functions (IMFs), each with a relative frequency scale. Third, Empirical Bayes Threshold (EBT) is applied on non-linear IMF to smooth out. Fourth, predicted models of denoised and decomposed IMFs are established by learning the feature values of the Support Vector Machine (SVM). Finally, the ensemble predicted results are formulated by adding the predicted IMFs. The proposed model is demonstrated using daily river inflow data from four river stations of the Indus River Basin (IRB) system, which is the largest water system in Pakistan. To fully illustrate the superiority of our proposed approach, the SSA-VMD-EBT-SVM hybrid model was compared with SSA-VMD-SVM, VMD-SVM, Empirical Mode Decomposition (EMD) based i.e., EMD-SVM, SSA-EMD-SVM, Ensemble EMD (EEMD) based i.e., EEMD-SVM and SSA-EEMD-SVM. We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Therefore, SSA-VMD-EBT-SVM model can be used for water resources management and power-generating systems using non-linear time series data.

Comparison of the hydrological time series modeling by the floods in river Indus of Pakistan

“…Nazir et al, in 2019, employed Variational Mode Decomposition (VMD) model that is based on a denoising technique called singular spectrum analysis (SSA), Empirical Bayes Threshold (EBT), and Support Vector Machine (SVM). 37 They applied these models to predict the daily river inflow of the Indus River Basin and compared the proposed model with others. Results showed that the suggested gave superior results and is validated for power-generating systems and water resources management.…”

A Sequential Structure for Water Inflow Forecasting in Coal Mines Integrating Feature Selection and Multi-Objective Optimization

“…Adarsh et al [21] adopted the multivariate empirical mode decomposition (MEMD) to perform the multiscale characterization of hydroclimatic time series, where the forecasting accuracy is significantly improved. Nazir et al [22] compared the improvements achieved by the combined models adopting empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD), where the evaluations for the results illustrate that the VMD-based model possesses higher prediction previous. For the well-investigated decomposition approaches including EMD, EEMD and VMD, the modalaliasing that is existed in EMD and is modified by EEMD to some extent may pose a challenge of accurate prediction for the decomposed components.…”