An Advanced Deep Learning model for Predicting Groundwater Level

Abstract

Abstract Groundwater level prediction is important for effective water management. Accurately predicting groundwater levels allows decision-makers to make informed decisions about water allocation, groundwater abstraction rates, and groundwater recharge strategies. Groundwater level prediction can also be used to develop more effective drought preparedness plans to mitigate the impact of water scarcity. In this study, we introduce a new model called self-attention (SA) temporal convolutional network (SATCN)-long short term memory neural network (SATCN-LSTM) model to predict groundwater level. The new model combines the advantages of the SATCN model and the LSTM model to overcome the limitations of the LSTM model. The SATCN model uses skip connections and self-attention mechanisms to overcome the vanishing gradient problem of the LSTM model, identify relevant and irrelevant data, and capture short-, and long-term dependencies of time series data. The new model was used to predict GWL in a large basin. Meteorological data were used to predict GWL. The SATCN-LSTM model outperformed the other models. The SATCN-LSTM model had the lowest mean absolute error (MAE) of 0.06, followed by the self-attention (SA) temporal convolutional network (SATCN) model with an MAE of 0.09. The SALSTM model had an MAE of 0.12, while the TCN-LSTM, TCN, and LSTM models had MAEs of 0.14, 0.15, and 0.17, respectively. The SATCN-LSTM model had the lowest root mean square error (RMSE) of 0.08, followed by SATCN with an RMSE of 0.11. The results of the SATCN-LSTM model provide valuable insights into the dynamics of groundwater systems. By accurately predicting groundwater levels, the SATCN-LSTM model can help ensure that groundwater resources are used sustainably and efficiently.