Comparison of a conceptual rainfall-runoff model with an artificial neural network model for streamflow prediction

Accurate streamflow forecasting can help minimizing the negative impacts of hydrological events such as floods and droughts. To address this challenge, we explore here artificial neural networks models (ANNs) for streamflow forecasting. These models, which have been proven successful in other fields, may offer improved accuracy and efficiency compared to traditional conceptually-based forecasting approaches.

The goal of this study is to compare the performance of a traditional conceptual rainfall-runoff (hydrological) model with an artificial neural network (ANN) model for streamflow forecasting. As a test case, we use the Severn catchment in the United Kingdom. The adopted ANN model has a long short-term memory (LSTM) architecture with two hidden layers, each with 256 neurons. The model is trained on a 25-year dataset from 1988 to 2013 and tested on a 3-year dataset (from 2014 to 2016). It is also validated on a 3-year dataset (from 2017 to 2020, 2019 being a particularly wet year), to assess its performance in extreme hydrological conditions. The study focuses on daily and hourly predictions.

To conduct this study, the conceptual hydrological model called Superflex is used as a benchmark. Both models are first evaluated using the Nash-Sutcliffe Efficiency (NSE) score. To enable a fair and accurate comparison, both models share the same inputs (i.e. meteorological forcings: total precipitation, daily maximum and minimum temperatures, daylight duration, mean surface downward short wave radiation flux, and vapor pressure). The ANN model was implemented using the Neuralhydrology library developed by F. Kratzert.

In our study, we found that LSTM model is able to provide more accurate one-day forecasts than the  hydrological model Superflex. For the daily predictions, the average NSE score using the LSTM model is 0.85 (with an average NSE score of 0.99 for training period, and 0.85 for validation period), which is higher than the NSE score of 0.74 achieved by the Superflex model (with a score of 0.84 for training period).

The hourly prediction using NSE with the superflex model had a score of 0.88, with a score of 0.7 during training. The LSTM model had an average NSE score of 0.87, with an average score of 0.99 during training and an average score of 0.85 during validation.

These results were obtained without adjusting the hyperparameters and by training the model only on data from the Severn watershed.The ANN model has demonstrated promising results compared to a state-of-the-art conceptual hydrological model in our studies. We will further compare both models using different training dataset periods, and different catchements. These additional tests will provide more information on the capabilities of the LSTM model and help to confirm its effectiveness.