Runoff predictive capability of a simple LSTM model versus a proven conceptual model between diverse hydrological regimes

Abstract

In the field of deep learning, LSTM lies in the category of recurrent neural network architectures. The distinctive capability of LSTM is learning non-linear long-term dependency structures. This makes LSTM a promising candidate for prediction tasks in non-linear time dependent systems such as prediction of runoff in a catchment. This work presents a comparative framework between an LSTM model and a proven conceptual model, namely GR4J. Performance of the two models is studied with respect to length of study period, surface area, and hydrological regime of 491 gauged French catchments covering a wide range of geographical and hydroclimatic conditions.