Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level

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. This study presents a novel model, the self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM), for groundwater level prediction. The SATCN-LSTM model combines the advantages of the SATCN and LSTM models to overcome the limitations of the LSTM model. By utilizing skip connections and self-attention mechanisms, the SATCN model addresses the vanishing gradient problem, identifies relevant data, and captures both short- and long-term dependencies in time series data. By demonstrating the improved performance of the SATCN-LSTM model in terms of mean absolute error and root mean square error (RMSE), and by comparing these results with those reported in previous papers, we have highlighted the advancements and contributions of the proposed model. By improving prediction accuracy, the SATCN-LSTM model enables decision-makers to make informed choices regarding water allocation, groundwater abstraction rates, and drought preparedness. The SATCN-LSTM model contributes to the sustainable and efficient use of groundwater resources by providing reliable information for decision-making processes. The SATCN-LSTM model combines the temporal convolutional network (TCN) architecture with LSTM. TCN is known for its ability to capture short-term dependencies in time series data, while LSTM is effective at capturing long-term dependencies. By integrating both architectures, the SATCN-LSTM model can capture the complex temporal relationships at different scales, leading to improved prediction accuracy. 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 0.09, followed by the self-attention (SA) temporal convolutional network (SATCN) model with an MAE of 0.12. The SALSTM model had an MAE of 0.16, while the TCN-LSTM, temporal convolutional network (TCN), and LSTM models had MAEs of 0.17, 0.22, and 0.23, respectively. The SATCN-LSTM model had the lowest root mean square error of 0.14, followed by SATCN with an RMSE of 0.15. The study results indicated that the SATCN-LSTM model was a robust tool for predicting groundwater level.

Keywords: Deep learning; Feature extraction; Groundwater management; Predictive models.