Use of long short-term memory network (LSTM) in the reconstruction of missing water level data in the River Seine

ABSTRACT

This paper aims to fill in the missing time series of hourly surface water levels of some stations installed along the River Seine, using the long short-term memory (LSTM) algorithm. In our study, only the water level data from the same station, containing many missing parts, were used as input and output variables, in contrast to other works where several features are available to take advantage of e.g. other station data/physical variables. A sensitive analysis is presented on both the network properties and how the input and output data are reentered to better determine the appropriate strategy. Numerous scenarios are presented, each an updated version of the previous one. Ultimately, the final version of the model can impute missing values of up to one year of hourly data with great flexibility (one-year Root-Mean-Square Error (RMSE) = 0.14 m) regardless of the location of the missing gaps in the series or their size.

Graphical abstract

KEYWORDS:

• missing data imputation
• hydrology
• water level
• River Seine
• long short-term memory (LSTM)
• deep learning
• machine learning

Editor S. Archfield; Associate Editor A. Agarwal

Editor S. Archfield; Associate Editor A. Agarwal

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by The Normandy Region [1].