Abstract
Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA–ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R 2), Nash–Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA–ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA–ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.
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Acknowledgments
This research was financially supported by the National Natural Science Foundation of China (NSFC) with grant number 41402202, Specialized Research Fund for the Doctoral Program of Higher Education (3M213BU81425), and Seed Science Foundation from Ministry of Education of China (450060488107). Special gratitude is given to editor Abdullah and Pradhan for their efforts on treating and evaluating the work, and the valuable comments of the other two anonymous reviewers are also greatly acknowledged.
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Yang, Q., Hou, Z., Wang, Y. et al. A comparative study of shallow groundwater level simulation with WA–ANN and ITS model in a coastal island of south China. Arab J Geosci 8, 6583–6593 (2015). https://doi.org/10.1007/s12517-014-1706-2
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DOI: https://doi.org/10.1007/s12517-014-1706-2