Accurate and reliable groundwater level forecasting\nmodels can help ensure the sustainable use of a\nwatershedââ?¬â?¢s aquifers for urban and rural water supply. In\nthis paper, three time series analysis methods, Holtââ?¬â??Winters\n(HW), integrated time series (ITS), and seasonal autoregressive\nintegrated moving average (SARIMA), are\nexplored to simulate the groundwater level in a coastal\naquifer, China. The monthly groundwater table depth data\ncollected in a long time series from 2000 to 2011 are\nsimulated and compared with those three time series\nmodels. The error criteria are estimated using coefficient of\ndetermination (R2), Nashââ?¬â??Sutcliffe model efficiency coefficient\n(E), and root-mean-squared error. The results indicate\nthat three models are all accurate in reproducing the\nhistorical time series of groundwater levels. The comparisons\nof three models show that HW model is more accurate\nin predicting the groundwater levels than SARIMA\nand ITS models. It is recommended that additional studies\nexplore this proposed method, which can be used in turn to\nfacilitate the development and implementation of more\neffective and sustainable groundwater management\nstrategies.
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