Abstract The optimal management of water resources depends on accurate and reliable streamflow prediction. Therefore, researchers have become interested in the development hybrid approaches recent years to enhance performance modeling techniques for predicting hydrological variables. In this study, models based variational mode decomposition (VMD) machine learning such as random forest (RF) K -...

To reduce the influence of random fluctuation on wind power prediction, a new ultra-short-term prediction model, based wavelet decomposition (WD), variational mode (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method double LSSVM, where sequence decomposed by WD into low- high-frequency components, which are further VMD to obtain many modal components w...

The Department of Health has recently published a report from the CFS/ME Working Group which concluded that chronic fatigue syndrome (CFS) should be recognized as a chronic illness. Symptoms consistent with CFS are often reported by people who consider their health has been affected by exposure to pesticides, but the Working Group concluded that this type of exposure is not a common trigger for...

Accurate power load forecasting plays an integral role in systems. To achieve high prediction accuracy, models need to extract effective features from raw data, and the training of needs a large amount data. However, data sharing will require disclosure private participants. address this issue, we combined variational mode decomposition (VMD), federated k-means clustering algorithm (FK), Secure...

Abstract Strong noise, nonlinearity, and complex frequency are the remarkable symbols of vibration signals when rolling bearing failure. So a new method that improved accuracy fault diagnosis is proposed based on variational mode decomposition (VMD) grey wolf algorithm optimized extreme learning machine (GWO-ELM). The signal decomposed by VMD, Intrinsic Mode Component (IMF) used for fuzzy entro...

Short-term power load forecasting is of great significance for the reliable and safe operation systems. In order to improve accuracy short-term forecasting, problems random fluctuation in complexity load-influencing factors, this paper proposes a two-stage method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), optimize variational mode decomposition (VMD) feature enginee...

The problem of low digging efficiency and mining imbalance due to outdated technology degree equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key roadheading equipment. Accurate lithology significantly affects automatic control roadheader cutting conditions. Completing process rock wall by a involved following steps: building tunnelin...

Although the technical requirements for feature extraction of ship radiated noise (SRN) in fields national defense and economy increase with each passing day, complexity marine environment makes SRN difficult. The traditional method based on variational mode decomposition (VMD) is widely used SRN. Nevertheless, use VMD greatly affected by parameters. In this paper, butterfly optimization algori...

Tides in fluvial estuaries are distorted by non-stationary river discharge, which makes the analysis of estuarine water levels less accurate when using conventional tidal method. As a powerful and widely-used method for nonlinear time series, application Variational Mode Decomposition (VMD) to tides is nonexistent. This paper aims illustrate verify suitability VMD as new tool tides. The efficie...