Neural networks in catchment hydrology: A comparative study of different algorithms in an ensemble of ungauged basins in Germany

Abstract. This study presents a comparative analysis of different neural network models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting discharge within ungauged basins in Hesse, Germany. All models were trained on 54 catchments with 28 years of daily meteorological data, either including or excluding 11 static catchment attributes. The training process of each model scenario combination was repeated 100 times, using a Latin Hyper Cube Sampler for the purpose of hyperparameter optimisation with batch sizes of 256 and 2048. The evaluation was carried out using data from 35 additional catchments (6 years) to ensure predictions in basins that were not part of the training data. This evaluation assesses predictive accuracy, computational efficiency concerning varying batch sizes and input configurations and conducted a sensitivity analysis of various hydrological and meteorological. The findings indicate that all examined artificial neural networks demonstrate significant predictive capabilities, with a CNN model exhibiting slightly superior performance, closely followed by LSTM and GRU models. The integration of static features was found to improve performance across all models, highlighting the importance of feature selection. Furthermore, models utilising larger batch sizes displayed reduced performance. The analysis of computational efficiency revealed that a GRU model is 41 % faster than the CNN and 59 % faster than the LSTM model. Despite a modest disparity in performance among the models (<3.9 %), the GRU model's advantageous computational speed renders it an optimal compromise between predictive accuracy and computational demand.