CNN-Bi LSTM Neural Network for Simulating Groundwater Level

Abstract

Keywords Abstract Bayesian Optimization, CNN, Bi LSTM, Groundwater level, Deep Learning. Managing groundwater resources affected by varying climatic conditions requires applying reliable and precise forecasts of groundwater levels. Hence, we investigated the implementation of deep learning neural network called CNN-Bi LTM, which combines convolutional neural network layers and bidirectional long-short term memory layers (Bi LSTM) models for forecasts of groundwater levels in a well affected by pumping for irrigation. The CNN-BiLSTM model was trained with hourly groundwater level data for Jan 2021-Dec 2021, and the data was divided into 70% for training and 30% for testing. Besides, Bayesian optimization was used to find the best range of variables for the model, such as the number of Bi LSTM units, the number of Bi LSTM layers, and the initial learning rate. Also, the Adaptive Moment Estimation (Adam) is used to calculate adaptive learning rates. As a result, the model showed promising results in the taring stage with a regression value equal to 0.9173. In comparison, the model showed acceptable results in the testing stage with regression equal to 0.6324, and the optimization duration lasted for 21 hours. Further, the optimization method showed that the best number of Bi LSTM units is 192, the best number of Bi LSTM layers is two layers, and the best initial learning rate is 0.01.