A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition
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 ensemb...
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| Format: | Article |
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MDPI AG
2018-06-01
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| Series: | Water |
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| Online Access: | http://www.mdpi.com/2073-4441/10/6/730 |
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| author | Yicheng Gong Zhongjing Wang Guoyin Xu Zixiong Zhang |
| author_facet | Yicheng Gong Zhongjing Wang Guoyin Xu Zixiong Zhang |
| author_sort | Yicheng Gong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T09:56:13Z |
| format | Article |
| id | doaj.art-41a30e2748f3474aaa94c083b235e0ab |
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| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-12T09:56:13Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-41a30e2748f3474aaa94c083b235e0ab2022-12-22T03:37:40ZengMDPI AGWater2073-44412018-06-0110673010.3390/w10060730w10060730A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode DecompositionYicheng Gong0Zhongjing Wang1Guoyin Xu2Zixiong Zhang3Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaThe 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.http://www.mdpi.com/2073-4441/10/6/730groundwater levelensemble empirical mode decompositionartificial neural networksupport vector machineadaptive neuro fuzzy inference system |
| spellingShingle | Yicheng Gong Zhongjing Wang Guoyin Xu Zixiong Zhang A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition Water groundwater level ensemble empirical mode decomposition artificial neural network support vector machine adaptive neuro fuzzy inference system |
| title | A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition |
| title_full | A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition |
| title_fullStr | A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition |
| title_full_unstemmed | A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition |
| title_short | A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition |
| title_sort | comparative study of groundwater level forecasting using data driven models based on ensemble empirical mode decomposition |
| topic | groundwater level ensemble empirical mode decomposition artificial neural network support vector machine adaptive neuro fuzzy inference system |
| url | http://www.mdpi.com/2073-4441/10/6/730 |
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