Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management

The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive integrated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model’s accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.