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Using LSTM to monitor continuous discharge indirectly with electrical conductivity observations
  • Yong Chang,
  • Benjamin Mewes,
  • Andreas Hartmann
Yong Chang
Hohai University, Hohai University

Corresponding Author:wwwkr@163.com

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Benjamin Mewes
Water Resources and Environmental Engineering, Water Resources and Environmental Engineering
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Andreas Hartmann
University of Freiburg, University of Freiburg
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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.