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Open AccessArticle

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

by 1,2,3, 1,3,*, 1,3,*, 1,3, 1,3 and 1,2,3
1
Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Qilian Mountains Eco-Environment Research Center in Gansu Province, Lanzhou 730000, China
*
Authors to whom correspondence should be addressed.
Water 2021, 13(2), 139; https://doi.org/10.3390/w13020139
Received: 12 November 2020 / Revised: 25 December 2020 / Accepted: 6 January 2021 / Published: 9 January 2021
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: uncertainty analysis; groundwater level prediction; hybrid predictive model; variational mode decomposition; Boruta technique; extreme learning machine uncertainty analysis; groundwater level prediction; hybrid predictive model; variational mode decomposition; Boruta technique; extreme learning machine
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MDPI and ACS Style

Wu, M.; Feng, Q.; Wen, X.; Yin, Z.; Yang, L.; Sheng, D. Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level. Water 2021, 13, 139. https://doi.org/10.3390/w13020139

AMA Style

Wu M, Feng Q, Wen X, Yin Z, Yang L, Sheng D. Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level. Water. 2021; 13(2):139. https://doi.org/10.3390/w13020139

Chicago/Turabian Style

Wu, Min; Feng, Qi; Wen, Xiaohu; Yin, Zhenliang; Yang, Linshan; Sheng, Danrui. 2021. "Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level" Water 13, no. 2: 139. https://doi.org/10.3390/w13020139

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