Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations.

• Long short-term memory neural network is proposed for groundwater prediction. • The proposed method relies only on in-situ piezometric observations. • The model is used for daily and monthly prediction for the Edward aquifer. • The results highlight the importance of long-term, groundwater level data. The application of neural networks (NN) in groundwater (GW) level prediction has been shown promising by previous works. Yet, previous works have relied on a variety of inputs, such as air temperature, pumping rates, precipitation, service population, and others. This work presents a long short-term memory neural network (LSTM-NN) for GW level forecasting using only previously observed GW level data as the input without resorting to any other type of data and information about a groundwater basin. This work applies the LSTM-NN for short-term and long-term GW level forecasting in the Edwards aquifer in Texas. The Adam optimizer is employed for training the LSTM-NN. The performance of the LSTM-NN was compared with that of a simple NN under 36 different scenarios with prediction horizons ranging from one day to three months, and covering several conditions of data availability. This paper's results demonstrate the superiority of the LSTM-NN over the simple-NN in all scenarios and the success of the LSTM-NN in accurate GW level prediction. The LSTM-NN predicts one lag, up to four lags, and up to 26 lags ahead GW level with an accuracy (R2) of at least 99.89%, 99.00%, and 90.00%, respectively, over a testing period longer than 17 years of the most recent records. The quality of this work's results demonstrates the capacity of machine learning (ML) in groundwater prediction, and affirms the importance of gathering high-quality, long-term, GW level data for predicting key groundwater characteristics useful in sustainable groundwater management. [ABSTRACT FROM AUTHOR]