Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network

Abstract

Monitoring groundwater level (GWL) over long time periods is critical in understanding the variability of groundwater resources in the present context of global changes. However, in Normandy (France) for example, GWLs have only been systematically monitored for ~20 to 50 years. This study evaluates Long Short-Term Memory (LSTM) neural network modeling to reconstruct GWLs, fill gaps and extend existing time-series. The approach is illustrated by using available monitoring fluctuations in piezometers implanted in the chalk aquifer in the Normandy region, Northern France. Here GWL data recorded over 50 years at 31 piezometers in northwestern Normandy is employed to perform GWL prediction. To optimize the network performance, the most influential factors that impact the accuracy of prediction are first determined, such as the network architecture, data quantity and quality. The resulting network is adopted to reconstruct measurements in the piezometers step by step with an increment of missing observation time. The approach requires no calibration for the time-lag in data processing and the implementation relies only on the groundwater level fluctuations to retrieve missing data in the targeted piezometers.