Abstract
Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.
Highlights
The results showed that the variational mode decomposition (VMD)-extreme learning machine (ELM) and the VMD-LSSVR models presented the best performance when compared with the VMD-based Artificial Neural Network (ANN) (VMD-ANN), discrete wavelet transform (DWT)based single models (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single models (ELM, LSSVR, and ANN)
It is worth noting that the determination of the number of K aforementioned was practiced by training data series; for the testing data series, the same number of modes is applied meaning that 10 intrinsic mode functions (IMFs) were decomposed
It can be seen that the VBELM models exhibited good prediction accuracy at the three sites, with high R and Nash-Sutcliffe efficiency coefficient (NS) values as well as low root mean square error (RMSE) and mean absolute error (MAE) values
Summary
The dwindling of groundwater level caused by external pressure of population increase, economic development, climate change and pollution over-exploitation is threatening the sustainability of water resources in arid areas [3,4]. The groundwater level can be assessed by physically-based and data-based models. A large quantity of hydrogeological data and the physical properties of groundwater such as hydraulic conductivity, volumetric water content and matric potential are hard to access even with expensive site investigations. An alternative artificial intelligence (AI) model, which formulates groundwater level nonlinearity merely by relying on the historical data or broader exogenous data as inputs, is necessary and significant under such conditions
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