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Water 2018, 10(6), 730; https://doi.org/10.3390/w10060730

A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition

1
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
2
State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing 100084, China
3
State Key Lab of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
*
Authors to whom correspondence should be addressed.
Received: 15 March 2018 / Revised: 3 May 2018 / Accepted: 29 May 2018 / Published: 4 June 2018
(This article belongs to the Section Water Resources Management and Governance)
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Abstract

The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources. View Full-Text
Keywords: groundwater level; ensemble empirical mode decomposition; artificial neural network; support vector machine; adaptive neuro fuzzy inference system groundwater level; ensemble empirical mode decomposition; artificial neural network; support vector machine; adaptive neuro fuzzy inference system
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Gong, Y.; Wang, Z.; Xu, G.; Zhang, Z. A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition. Water 2018, 10, 730.

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