Abstract
To construct an accurate and stable approach for water inflow forecasting, a series of advanced and effective techniques, such as variational mode decomposition (VMD), outlier robust extreme learning machine (ORELM) and multi-objective grey wolf optimizer (MOGWO), are appropriately integrated into this study. Considering that the influence of the mode number on the VMD decomposition effectiveness, such an argument is determined by observing the converged centre frequency distribution among the components. Then the characteristic items of water inflow series are extracted by VMD, thus obtaining a series of sub-components. Afterwards, ORELM is applied to predict each component, where the parameters of ORELM are optimized by MOGWO with multi-objective functions including forecasting accuracy and stability. Correspondingly, the aggregation of all components’ prediction values is considered as the final results. The experimental results obtained by performing eight various models on real-time data demonstrate that the supplementary modules achieve positive effects on the improvement of prediction accuracy, where the proposed model implements an average performance promotion of 48.43% compared with the contrastive models.
Highlights
The precise water inflow prediction for deep mine has received widespread focus in the past few decades, which plays a vital role in designing sound mine drainage system and practical arrangements for water prevention [1].A brief classification for the existing water inflow prediction approaches can be claimed as analytical methods, numerical simulations and data-based uncertainty models
The simple decreasing of metrics root-mean-square error (RMSE), MAE and MAPE is adopted to observe the differences between two models, where the specific formula is expressed as (17)
Algorithm 1 Optimized outlier robust extreme learning machine (ORELM) Based on multi-objective grey wolf optimizer (MOGWO) Fitness Function: min f1 =
Summary
The precise water inflow prediction for deep mine has received widespread focus in the past few decades, which plays a vital role in designing sound mine drainage system and practical arrangements for water prevention [1]. A brief classification for the existing water inflow prediction approaches can be claimed as analytical methods, numerical simulations and data-based uncertainty models. For the numerical simulations, which focus on estimation modelling by finite element methods applying the permeability coefficient, recharge intensity and mechanical parameters, have been widely investigated among the past decades. Oda [3] proposed an equivalent model by coupling the seepage field, stress field of the rock mass and combining the stress tensor method with the permeability tensor
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