Uncertainty assessment of LSTM based groundwater level predictions

Abstract

ABSTRACT Due to the underlying uncertainty in groundwater level (GWL) modelling, point prediction of GWLs does not provide sufficient information. Moreover, the insufficiency of data on subjects such as illegal exploitation wells and wastewater pounds, which are untraceable, underlines the importance of evolved uncertainty in the groundwaters of the Ardabil plain. Thus, estimating prediction intervals (PIs) for groundwater modelling can be an important step. In this paper, PIs were estimated for GWLs of selected piezometers of the Ardebil plain in Iran using the artificial neural network (ANN)-based lower upper bound estimation (LUBE) method. The classic feedforward neural network (FFNN) and deep-learning-based long short-term memory (LSTM) were used. GWL data of piezometers and hydrological data (1992–2018) were applied for modelling. The results indicate that LSTM outperforms FFNN in both PI and point prediction tasks. LSTM-based LUBE was found to be superior to FFNN-based LUBE, providing an average 25% lower coverage width criterion (CWC). PIs estimated for piezometers with high transmissivity resulted in 50% lower CWC than PIs estimated for piezometers in areas with lower transmissivity.