Using LSTM to monitor continuous discharge indirectly with electrical
conductivity observations
Abstract
Due to EC’s easy recordability and the existence of a strong correlation
between EC and discharge in certain catchments, EC is a potential
predictor of discharge. This potential has yet to be widely addressed.
In this paper, we investigate the feasibility of using EC as a proxy for
long-term discharge monitoring in a small karst catchment where EC
always shows a negative correlation with the spring’s discharge. Given
their complex relationship, a special machine learning architecture,
LSTM (Long Short Term Memory), was used to handle the mapping from EC to
discharge. The results indicate, based on LSTM, that the spring’s
discharge can be predicted well with EC, particularly in storms when the
dilution dominates the EC dynamic; however, the prediction may have
relatively large uncertainties in the small or middle recharge events. A
small number of discharge observations are sufficient to obtain a robust
LSTM for the long-term discharge prediction from EC, indicating the
practicality of recording EC in ungauged catchments for indirect
discharge monitoring. Our study also highlights that the random or
fixed-interval discharge measurement strategy, which covers various
climate conditions, is more informative for LSTM to give robust
predictions. While our study is implemented in a karst catchment, the
method is also suitable for non-karst catchments where there is a strong
correlation between EC and discharge.