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Groundwater Depth Forecasting Using Configurational Entropy Spectral Analyses with the Optimal Input
Author(s) -
Guo Tianli,
Song Songbai,
Shi Jihai,
Li Jun
Publication year - 2019
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/gwat.12968
Subject(s) - groundwater , autoregressive model , mean squared error , entropy (arrow of time) , correlation coefficient , series (stratigraphy) , hydrology (agriculture) , autoregressive integrated moving average , environmental science , mathematics , statistics , soil science , time series , geology , geotechnical engineering , thermodynamics , paleontology , physics
Accurate groundwater depth forecasting is particularly important for human life and sustainable groundwater management in arid and semi‐arid areas. To improve the groundwater forecasting accuracy, in this paper, a hybrid groundwater depth forecasting model using configurational entropy spectral analyses (CESA) with the optimal input is constructed. An original groundwater depth series is decomposed into subseries of different frequencies using the variational mode decomposition (VMD) method. Cross‐correlation analysis and Shannon entropy methods are applied to select the optimal input series for the model. The ultimate forecasted values of the groundwater depth can be obtained from the various forecasted values of the selected series with the CESA model. The applicability of the hybrid model is verified using the groundwater depth data from four monitoring wells in the Xi'an of Northwest China. The forecasting accuracy of the models was evaluated based on the average relative error (RE), root mean square error (RMSE), correlation coefficient ( R ) and Nash‐Sutcliffe coefficient (NSE). The results indicated that comparing with the CESA and autoregressive model, the hybrid model has higher prediction performance.