Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network

Abstract

In this study, mean monthly groundwater level (GWL) fluctuations of four different monitoring piezometers are forecasted in an agricultural area by developing new proposed structures of BiLSTM based neural network models. For developing models, 186 monthly measured water table depth during time period (Sep 2001–Feb 2017) are employed. Due to a lack of meteorological variables records, the module of sequence-to-one is employed. By tuning several hyperparameters such as the number of hidden units (NHU), kind of state activation function (SAF), learning dropout rate (P-rate), and network topology pattern (NTP), the performance of designed models is improved. Based on results of modelling, in all designed models, the optimal P-rate is obtained 0.5, and the running time decrease by increasing P-rate in the same model. Since harsh fluctuations of GWL, the general Single-LSTM mode (1) and Single-BiLSTM model (2) perform too weak compared to other models. Nonetheless, by deepening models through adding the suitable hidden layers instead of many numbers of nodes, the performance of Single-BiLSTM model is improved strikingly. Based on statistical evaluation metrics, violin plot, and also TLP (Total Learnable Parameters) value, novel proposed model (5) (simple Double-BiLSTM model combined by a Multiplication layer (×)) is selected as the superior model. In the piezometer 4 with a range of 4.49 (m), the model (5) yielded in an R2 of 0.89 and an RMSE of 0.17 (m), while in the same physical characteristics, the simple Double-BiLSTM model (3) yielded in an R2 of 0.77 and an RMSE of 0.25 (m).